Hyperspectral sensing system and processing methods for hyperspectral data

ABSTRACT

A hyperspectral sensing device may include an optical collector configured to collect light and to transfer the collected light to a sensor having spectral resolution sufficient for sensing hyperspectral data. In some examples, the sensor comprises a compact spectrometer. The device further comprises a power supply, an electronics module, and an input/output hub enabling the device to transmit acquired data (e.g., to a remote server). In some examples, a plurality of hyperspectral sensing devices are deployed as a network to acquire data over a relatively large area. Methods are disclosed for performing dark-current calibration and/or radiometric calibration on data obtained by the hyperspectral sensing device, and/or another suitable device. Data obtained by the device may be represented in a functional basis space, enabling computations that utilize all of the hyperspectral data without loss of information.

CROSS-REFERENCES

This application is a continuation in part of U.S. patent applicationSer. No. 16/366,635, filed Mar. 27, 2019, which claims the benefit under35 U.S.C. § 119(e) of U.S. Provisional Patent Application Ser. No.62/648,779, filed Mar. 27, 2018, the entireties of which are herebyincorporated by reference for all purposes.

FIELD

This disclosure relates to systems and methods for hyperspectralsensing, including methods of processing, calibrating, and/or analyzinghyperspectral data.

INTRODUCTION

Optical characteristics of aquatic, terrestrial, and atmosphericenvironments may be measured to detect the presence and/or abundance ofvarious substances. For example, the wavelength-dependent intensity oflight reflected from or absorbed within oceans, lakes, and other bodiesof water may be monitored over time to quantify gradual or suddenchanges in concentrations of sediment and/or biological matter.Similarly, optical absorption and scattering from land may be monitoredto obtain spatial and/or temporal distributions of vegetation, minerals,and/or other substances.

In many cases, much of the useful information contained in this opticaldata involves wavelength- or frequency-dependent characteristics of themeasured light. Systems configured to measure light at a high spectralresolution are therefore desirable. A hyperspectral sensor, which canmeasure the spectrum of light at each spatial pixel in a region ofinterest, would provide a large amount of high-resolution data. However,known hyperspectral sensing systems suffer from a number of drawbacks.For example, known systems are typically limited to a single mode ofdeployment (e.g., above-water deployment only, underwater deploymentonly, etc.), and are unsuitable for autonomous deployment due to factorssuch as size, cost, power requirements, and sensitivity to vibration.Accordingly, there is a need for hyperspectral sensing systemsconfigured for field use in water-quality assessment, remote sensing,and/or other similar applications.

SUMMARY

The present disclosure provides systems, apparatuses, and methodsrelating to hyperspectral sensing and data.

In some examples, a computer-implemented method for predictingground-truth data corresponding to remotely measured data comprisesacquiring a ground-truth spectrum corresponding to light measured at afirst location at a first time; acquiring first remote spectral datacorresponding to the first location at the first time; determining firstweighting coefficients of a ground-truth function representing theground-truth spectrum in a functional basis space; determining secondweighting coefficients of a first remote function representing the firstremote spectral data in the functional basis space; determining acorrelating relationship predicting the first weighting coefficientsbased on the second weighting coefficients; acquiring second remotespectral data and determining third weighting coefficients of a secondremote function representing the second remote spectral data in thefunctional basis space; and using the correlating relationship topredict, based on the third weighting coefficients, projectedground-truth weighting coefficients corresponding to a projectedground-truth function representing a projected ground-truth spectrum inthe functional basis space.

In some examples, a method for measuring a dark-current-correctedspectrum comprises measuring a first spectrum of light using aphotosensitive detector having a first integration time measuring aplurality of second spectra using the photosensitive detector, each ofthe second spectra corresponding to a different integration time of thephotosensitive detector; estimating a respective dark-currentcontribution for each of the second spectra based on a respective lowestvalue of each of the second spectra; computing a first estimateddark-current contribution for the first spectrum based on the estimateddark-current contributions for the second spectra and the firstintegration time; and subtracting the first estimated dark-currentcontribution from each value of the first spectrum to produce adark-current-corrected spectrum.

In some examples, a method of measuring a radiometrically calibratedspectrum using a spectral sensor deployed at an outdoor location withoutrelocating the spectral sensor comprises measuring a spectrum of lightusing a spectral sensor deployed at an outdoor location; normalizing themeasured spectrum using a selected normalizing factor; computing asimulated spectrum based on a simulation of at least a portion of theatmosphere at the outdoors location, the simulation including one ormore adjustable parameters; adjusting at least one of the adjustableparameters to produce an adjusted simulated spectrum matching themeasured spectrum according to one or more predefined criteria;determining a mathematical transformation capable of transforming themeasured spectrum, such that at least a portion of the transformedmeasured spectrum approximates at least a portion of the adjustedsimulated spectrum; and performing the mathematical transformation onthe measured spectrum to produce a radiometrically calibrated spectrum.

Features, functions, and advantages may be achieved independently invarious embodiments of the present disclosure, or may be combined in yetother embodiments, further details of which can be seen with referenceto the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic depiction of an illustrative hyperspectral imagein accordance with aspects of the present teachings.

FIG. 2 is a schematic depiction of an illustrative hyperspectral sensingdevice in accordance with aspects of the present teachings.

FIG. 3 is a is a schematic diagram of an illustrative compactspectrometer in accordance with aspects of the present teachings.

FIG. 4 is a schematic diagram depicting a hyperspectral sensing systemperforming a measurement on a discrete sample, in accordance withaspects of the present teachings.

FIG. 5 is a schematic diagram depicting a hyperspectral sensing systemperforming a measurement while immersed in a water sample, in accordancewith aspects of the present teachings.

FIG. 6 is an illustrative hyperspectral sensing device configured forperforming angularly-resolved hyperspectral measurements, according toaspects of the present teachings.

FIG. 7 is a schematic diagram of an illustrative optical collectorconfigured to simultaneously collect radiance from two differentdirections in accordance with aspects of the present teachings.

FIG. 8 is an isometric view of an illustrative chopper wheel configuredto modulate light according to aspects of the present teachings.

FIG. 9 is a schematic diagram of an illustrative optical collectorincluding a convex reflector, in accordance with aspects of the presentteachings.

FIG. 10 is a schematic diagram of an illustrative optical collectorincluding a movable reflector, in accordance with aspects of the presentteachings.

FIG. 11 is a schematic diagram of an illustrative optical collectorincluding a movable dispersing element, in accordance with aspects ofthe present teachings.

FIG. 12 is a schematic diagram of an illustrative optical collectorincluding a beamsplitter, in accordance with aspects of the presentteachings.

FIG. 13 is a schematic diagram of an illustrative optical collectorconfigured to simultaneously collect sky radiance, water radiance, andreference plaque radiance according to aspects of the presentdisclosure.

FIG. 14 is a schematic diagram of an illustrative optical collectorconfigured to simultaneously and/or sequentially measure sky radiance,water radiance, and/or reference plaque radiance according to aspects ofthe present disclosure.

FIG. 15 is a schematic diagram of an illustrative network ofhyperspectral sensing devices in accordance with aspects of the presentteachings.

FIG. 16 is a flow diagram depicting steps of an illustrative method forsimultaneous measurement of sky radiance and water radiance according tothe present teachings.

FIG. 17 is a flow diagram depicting steps of an illustrative method forperforming fluorescence, scattering, and/or attenuation measurements ona sample using a hyperspectral sensing system, in accordance withaspects of the present teachings.

FIG. 18 is a flow diagram depicting steps of an illustrative method forassessing water quality.

FIG. 19 is a flow diagram depicting steps of an illustrative method foracquiring hyperspectral data above water and underwater.

FIG. 20 is a schematic diagram depicting an illustrative electronicsmodule of a hyperspectral sensing system, in accordance with aspects ofthe present teachings.

FIG. 21 is a schematic diagram of an illustrative data processing systemin accordance with aspects of the present teachings.

FIG. 22 is a schematic diagram of an illustrative distributed dataprocessing system in accordance with aspects of the present teachings.

FIG. 23 is a flow diagram depicting steps of an illustrative method forobtaining dark-current-corrected spectral data.

FIG. 24 is a flow diagram depicting steps of an illustrative method forobtaining radiometrically corrected spectral data.

FIG. 25 is a flow diagram depicting steps of an illustrative method forobtaining data accurately representing water-leaving radiance.

FIG. 26 is a flow diagram depicting steps of an illustrative method forupdating remote spectral data using a correlation betweenfunctional-basis representations of another set of remote data andcorresponding ground-truth data.

DETAILED DESCRIPTION

Various aspects and examples of a hyperspectral sensing system, as wellas related methods, are described below and illustrated in theassociated drawings. Unless otherwise specified, a hyperspectral sensingsystem in accordance with the present teachings, and/or its variouscomponents, may contain at least one of the structures, components,functionalities, and/or variations described, illustrated, and/orincorporated herein. Furthermore, unless specifically excluded, theprocess steps, structures, components, functionalities, and/orvariations described, illustrated, and/or incorporated herein inconnection with the present teachings may be included in other similardevices and methods, including being interchangeable between disclosedembodiments. The following description of various examples is merelyillustrative in nature and is in no way intended to limit thedisclosure, its application, or uses. Additionally, the advantagesprovided by the examples and embodiments described below areillustrative in nature and not all examples and embodiments provide thesame advantages or the same degree of advantages.

This Detailed Description includes the following sections, which followimmediately below: (1) Definitions; (2) Overview; (3) Examples,Components, and Alternatives; (4) Advantages, Features, and Benefits;and (5) Conclusion. The Examples, Components, and Alternatives sectionis further divided into subsections A through P, each of which islabeled accordingly.

Definitions

The following definitions apply herein, unless otherwise indicated.

“Substantially” means to be more-or-less conforming to the particulardimension, range, shape, concept, or other aspect modified by the term,such that a feature or component need not conform exactly. For example,a “substantially cylindrical” object means that the object resembles acylinder, but may have one or more deviations from a true cylinder.

“Comprising,” “including,” and “having” (and conjugations thereof) areused interchangeably to mean including but not necessarily limited to,and are open-ended terms not intended to exclude additional, unrecitedelements or method steps.

Terms such as “first”, “second”, and “third” are used to distinguish oridentify various members of a group, or the like, and are not intendedto show serial or numerical limitation.

“AKA” means “also known as,” and may be used to indicate an alternativeor corresponding term for a given element or elements.

“Coupled” means connected, either permanently or releasably, whetherdirectly or indirectly through intervening components.

“Processing logic” means any suitable device(s) or hardware configuredto process data by performing one or more logical and/or arithmeticoperations (e.g., executing coded instructions). For example, processinglogic may include one or more processors (e.g., central processing units(CPUs) and/or graphics processing units (GPUs)), microprocessors,clusters of processing cores, FPGAs (field-programmable gate arrays),artificial intelligence (AI) accelerators, digital signal processors(DSPs), and/or any other suitable combination of logic hardware.

Overview

In general, a hyperspectral sensing system in accordance with aspects ofthe present teachings is configured to obtain hyperspectral data whiledeployed in the field (e.g., adjacent a body of water, underwater, on anaerial vehicle, and/or the like). Typically, the hyperspectral sensingsystem includes a sensor configured to measure a spectrum of light witha high spectral resolution, along with one or more optical assembliesconfigured to direct light to the sensor. For example, the system maycomprise one or more compact spectrometers (AKA miniature spectrometers)and suitable optics.

Hyperspectral data generally comprises an image composed of one or morespatial pixels, with high-resolution spectral information (e.g.,wavelength-dependent or frequency-dependent information) associated witheach pixel. The spectral information may include a measured light level(e.g., brightness, energy, power, and/or intensity of light) in each ofa plurality of narrow, adjacent spectral bands. Hyperspectral data maybe represented by a hyperspectral image, which may be thought of as athree-dimensional image or hyperspectral cube having two spatialdimensions and one spectral dimension. FIG. 1 depicts an illustrativehyperspectral image 20 comprising a plurality of two-dimensional images22 of a coastal region, wherein each two-dimensional image correspondsto a detected level of light within a respective spectral band. In otherwords, each pixel 25 of one of the two-dimensional images 22 isassociated with a level of light detected within the spectral bandassociated with that two-dimensional image.

Accordingly, as shown schematically in FIG. 1, a set 27 of pixels 25corresponding to a same location in each of the two-dimensional imagescomprises a spectrum of detected light levels for the correspondinglocation across all spectral bands in hyperspectral image 20. Thespectrum associated with set 27 may be illustrated as a plot 29depicting measured light level (e.g., percentage of light reflected) asa function of wavelength for the location associated with the selectedpixel 25. Hyperspectral image 20 comprises such a spectrum for eachspatial pixel in the image.

For simplicity, only a few two-dimensional images 22 are depicted inFIG. 1, and therefore the depicted hyperspectral image 20 comprises onlya few spectral bands. Typically, however, hyperspectral images includelight levels associated with at least tens or hundreds of adjacentand/or narrowly spaced spectral bands, such that the spectrum associatedwith a given pixel across the plurality of two-dimensional images (e.g.,a spectrum like that depicted in plot 29) is, to good approximation,continuous.

A hyperspectral sensing system may be referred to as a hyperspectralimaging system and/or a hyperspectral imager. As discussed below, thehyperspectral sensing system of the present disclosure is configured tolog (e.g., record) hyperspectral data and therefore may also be referredto as a hyperspectral logger, hyperspectral logging system,hyperspectral logging radiometer, and/or optical data logger.

A hyperspectral sensing system in accordance with aspects of the presentteachings is typically well suited for use in water-quality assessments,remote sensing, underwater deployment, and/or other field settings. Forexample, the system may comprise one or more devices that are small insize, lightweight, suitable for use in or adjacent water, relativelyinsensitive to vibration, and/or configured to acquire data withoutcareful alignment and/or frequent calibration. In some examples, ahyperspectral sensing system comprises a network of hyperspectralsensing devices distributed in a suitable location and configured tostore and/or transmit sensed data.

A hyperspectral sensing system in accordance with aspects of the presentteachings may be used to acquire hyperspectral data in a variety ofdeployment modalities. For example, the system typically has a lowphysical volume and a low weight and is therefore suitable for aerialdeployment, e.g., on an airplane, unmanned aerial vehicle, weatherballoon, and/or the like. In some examples, the system may also bedeployed underwater, e.g., on a watercraft, buoy, unmanned underwatervehicle, manually operated by a diver, and so on. Additionally, oralternatively, the system may be deployed on the ground, adjacent a bodyof water, and/or in any other suitable location. In some examples, thesystem is configured to simultaneously measure light incident frommultiple directions, e.g., from the sky and from a body of water.

In some examples, the system is used to acquire data from a discretesample (e.g., a sample of water and/or another fluid) collected by aprofiling system (e.g., a rosette comprising an array of Niskin bottles,Scotty bottles, and/or the like, and optionally including sensors suchas CTD sensors). In some examples, a sample of fluid is collected by aflow-through system configured to pump fluid into a sample chamber, andthe system acquires data from the sample within the sample chamber.These capabilities are discussed further below.

Examples, Components, and Alternatives

The following sections describe selected aspects of exemplaryhyperspectral sensing systems as well as related systems and/or methods.The examples in these sections are intended for illustration and shouldnot be interpreted as limiting the scope of the present disclosure. Eachsection may include one or more distinct embodiments or examples, and/orcontextual or related information, function, and/or structure.

A. Illustrative Hyperspectral Sensing Device

With reference to FIG. 2, this section describes an illustrativehyperspectral sensing device 30. Device 30 is an example of thehyperspectral sensing systems described above.

As shown in FIG. 2, which is a schematic depiction of device 30, thedevice includes an optical collector 35 configured to collect light froma sample spatial region. The sample region may include a landmass, abody of water, a portion of the atmosphere, and/or any other suitablearea or object of interest.

Collector 35 is further configured to transfer the collected light fromthe sample region to a sensor 40. Transferring the light to sensor 40typically includes steering the light toward the sensor and may furtherinclude minimizing optical distortions, aberrations, and/or stray light.In some examples, transferring the light to sensor 40 includes imagingthe sample region onto the sensor (e.g., onto a slit of the sensor, ontoa sensing element of the sensor, and/or the like).

Light collected by collector 35 includes light emitted by, reflected by,and/or transmitted through objects within the sample region viewable bycollector 35. The viewable region may be characterized by, e.g., anangular field of view (AFOV) and/or a horizontal or vertical field ofview (FOV) of collector 35, e.g., at a desired working distance from thecollector (e.g., at the object plane). The spectrum of the collectedlight (e.g., the intensity, brightness, radiance, and/or other level ofthe collected light as a function of wavelength) may be used tocharacterize objects within the sample region. For example, if thesample region includes a portion of a landmass, the collected light mayprimarily include light reflected from the landmass portion, and thespectrum of the reflected light may be used to infer mineral content ofthe landmass portion. The objects and/or portions of objects within theviewable sample region may be referred to as samples, and collectinglight from the samples (e.g., acquiring a single spectrum and/orsequentially acquiring a plurality of spectra of the same viewableregion) may be referred to as sampling.

Collector 35 may comprise any assembly of optical components suitablefor collecting light from the sample region and transferring thecollected light to sensor 40. For example, collector 35 may comprise anaperture, an entrance slit, an optical tube, and/or a fiber optic guide.An optical fiber configured to guide input light collected by anentrance aperture and/or fore-optic to the detector plane of sensor 40may be useful when objects or other physical obscurations are disposedbetween the sample and the sensor, or when it is desirable to physicallyseparate the sensor from the sample. For example, samples may bedifficult to physically access due to space available or fragileenvironments such as in proximity to under-water vegetation, rockformations, or coral structures. In other cases, a desirable or optimallocation of the optical sampling port on a deployment vessel, orreducing the instrument and vessel self-shading of the sample area, mayrequire separating the instrument from the sampling entrance port.

Additionally, or alternatively, collector 35 may include a compoundmirror and/or lens system with fore-optics; relay optics, diffusers,dispersive optical elements, and/or other homogenizing elements;polarizing elements; and/or adaptive telescope and/or microscopeobjectives with wavefront correction. In some examples, collector 35includes optics configured to increase the uniformity of a spatialdistribution (e.g., an angular distribution) of collected light. Forexample, collector 35 may include a diffusing collector with a cosineresponse, AKA a cosine corrector, configured to acquire a 180-degree FOVirradiance, and to accurately weight the angular distribution of anincoming incoherent light-field. A cosine corrector may be particularlyuseful for, e.g., measurements of sky irradiance or underwaterdownwelling or upwelling radiances. Light from an inhomogeneous sourcemay also be generally made more uniform by a ground-glass diffuser, afly's-eye homogenizer, or diffractive optical elements (DOE) withincollector 35.

In some examples, one or more linear polarizing optical elements and/orone or more circular polarizing optical elements are included incollector 35 to select and/or measure the polarization of the incominglight field as a function of wavelength and of angle of polarization.The angular orientation(s) of the polarizer(s) may be actuated manuallyand/or mechanically by automated and/or motorized control. Thepolarizing element or elements may be moved in or out of the opticalpath manually and/or mechanically by automated and/or motorized control.The polarizer(s) may be used, for example, to perform a hyperspectralpolarization measurement to identify specific substances (e.g.,substances having light-polarizing characteristics). A hyperspectralpolarization measurement may be performed, for example, using lightreflected from the surface of a body of water. Such light is typicallypolarized by the refraction and reflection at the air-water interface,and may be additionally polarized by the absorption from water moleculesor other suspended matter in the surface layer. A circular polarizer maybe used, for example, to reduce or amplify the light reflected from thesurface of a body of water.

In some examples, collector 35 is configured to be dynamically actuatedto vary the field of view, depth of focus, and/or other parameters. Forexample, collector 35 may include an entrance slit of adjustable size(e.g., an adjustable aperture, such as an iris). Optical diffusers, suchas ground glass or quartz diffusers, may be used to mix the angulardistribution of light across the sampled region. Additionally, oralternatively, positions of optical collector elements such as lensesmay be adjustable. For example, optical collector elements may bemounted on translation stages such that distances between sensor 40 andoptical collector elements is variable. In examples including adjustablecollector elements, the size (e.g., spatial extent) of the pixelssampled by device 30 may be variable while the distance between sensor40 and the sampled object is fixed. The spatial distribution sampled bydevice 30 may thus be varied without changing the position of sensor 40.Additionally, adjusting the position of the focal plane of a collectormay help to sample the imaging contribution of a desired best-focusplane. Collector 35 may include steering optics configured toselectively change the lateral location of the image field and therebyscan a larger sampling area than possible with a fixed samplinglocation.

In some examples, optical elements within collector 35 may beindividually adjustable and/or removable and replaceable. For example,optical collector elements may be mounted by bolts and/or screws to anoptical breadboard having threaded bores. Additionally, oralternatively, collector 35 may be configured to be detachable fromdevice 30 (e.g., from a housing of the device) and replaced with anothercollector. For example, a collector 35 configured to sample objects faraway from device 30 may be replaced with a collector configured tosample objects very close to the device. The ability to adjust and/orreplace collector 35 helps device 30 to collect data in a variety ofsettings and/or modes of deployment.

As described above, collector 35 is configured to transfer sampled lightinto sensor 40 (e.g., onto an entrance aperture of sensor 40). Sensor 40may comprise any suitable device configured to receive light and tomeasure the level (e.g., an energy, power, and/or intensity) of thereceived light in a plurality of narrow spectral bands. Typically,sensor 40 includes a dispersive optical element configured to spatiallyseparate spectral components of the received light, and one or moredetectors configured to measure light intensity at a plurality ofspatial points. The measured intensity at each spatial point correspondsto the intensity of light in a certain spectral band. The mappingbetween spatial points and spectral bands can be calculated based on,e.g., the dispersive properties of the dispersive element, the relativepositions between the dispersive element and the spatial points, and soon. Sensor 40 is typically a compact optoelectronic device, such as aspectrometer or mini-spectrometer (see FIG. 3 and associated descriptionin Section B). In some examples, however, sensor 40 may comprise anothertype of device, such as a spectrophotometer, a CCD array, a CMOS array,and/or the like.

Collector 35 may include optics configured to at least partially correctimperfections in the radiometric (spectral) or angular response ofsensor 40 arising from wavefront aberrations introduced by variouscomponents of the sensor (e.g., an entrance aperture, a dispersiveelement such as grating or prism, coatings, and/or sensorimperfections). Sensor 40 may be disposed in any suitable positionrelative to collector 35 to receive the light from the collector. Insome examples, sensor 40 may be positioned adjacent to and/or coupled tocollector 35.

As shown in dashed lines in FIG. 2, device 30 optionally includes asecond sensor 40, and may include a second collector 35 configured tocollect light and transfer the collected light to the second sensor. Inexamples wherein device 30 includes two or more sensors, collectorscorresponding to the two or more sensors may be configured to collectlight incident from different directions. For example, the firstcollector may transfer light incident from a first direction onto thefirst sensor, and the second collector may transfer light incident froma second direction onto the second sensor. In some examples, a singlecollector 35 is configured to direct light from two different directionsonto two different sensors. In examples wherein device 30 includes twoor more collectors, the two or more collectors may have differentoptical properties (e.g., different fields of view, focal lengths,and/or the like), or may be substantially identical. If two or moresensors are included, they may have different properties (e.g.,sensitivity, dynamic range, wavelength range, etc.) or may besubstantially identical. Use of two or more sensors may enable device 30to acquire data from two or more samples simultaneously. (However, seealso Section D below, describing illustrative collectors configured toenable simultaneous measurements with a single sensor.)

In addition to sensor 40, device 30 optionally includes one or moreauxiliary sensors 50 such as GPS receivers, thermometers, pressuresensors (e.g., for depth and/or altimetry), humidity sensors, CTDsensors (AKA Sonde sensors), dissolved oxygen sensors, compasses,inertial measurement units, real-time clock (RTC) oscillators,photodiodes, pH sensors, dissolved Nitrogen (nitrates) sensors,dissolved organic carbon sensors, dissolved inorganic carbon sensors,and/or any other suitable sensors. The fusion of multiple sensor inputsmay be very useful for precision optical measurements, because theoutput of one sensor (e.g., sensor 40 or one of auxiliary sensors 50)can be strongly coupled to a physical property measurable by a separatesensor. For example, the ability of sensor 40 to correctly measure thewavelength of light may depend linearly or nonlinearly on thetemperature of the sensor and therefore on the temperature of theenvironment in which the sensor is used. Based on the relationshipbetween measured wavelength and sensed temperature, a correction to thewavelength reading can be performed based on data measured by atemperature sensor in proximity to sensor 40. This improves the accuracyand stability of the obtained hyper-spectral data. In some examples,corrections based on temperature (or other data measured by auxiliarysensor 50) are performed on board device 30 (e.g., by an electronicsmodule) at the analog-circuit level and/or digitally (following theanalog-to-digital conversion). Additionally, or alternatively, thecorrection may be implemented on an external computer after the data istransferred off-board the hyperspectral data-logging device.

Additionally, or alternatively, device 30 may include a light source 60.Light source 60 may be configured to illuminate at least a portion ofthe sampling region with a light having a predetermined intensity,propagation direction, polarization, and/or range of wavelengths. Lightsource 60 may enable measurements involving fluorescence, absorption,scattering, and/or the like.

Device 30 further includes an electronics module 70. Electronics module70 includes a memory store 75 coupled to sensor 40 and configured tostore data obtained by the sensor. Optionally, memory store 75 may beconfigured to store data from sensor 40 in association with data fromauxiliary sensors 50 corresponding to, e.g., a timestamp correspondingto the time at which the data was collected, GPS coordinates, ambienttemperature, settings of the sensor, and/or the like. Memory store 75may be nonvolatile (e.g., configured to continue storing data whendisconnected from a power source) and/or volatile (e.g., configured todiscontinue storing data when disconnected from a power source). Inexamples including a volatile memory, using device 30 may includetransferring data from the volatile memory to an external storage deviceprior to disconnecting the memory from a power source.

Electronics module 70 may include processing logic 78 configured to,e.g., trigger sensor 40 and/or auxiliary sensors 50 to start and/or stopcollecting data, actuate adjustable elements of collector 35, write datafrom sensor 40 and/or auxiliary sensors 50 to memory store 75, and/orcommunicate with an input/output hub of device 30. For example, signalsfrom auxiliary sensors 50 may be read by electronics module 70, whichmay selectively trigger sensor 40 to perform measurements based on theauxiliary sensor readings (e.g., when the auxiliary sensor readingssatisfy one or more criteria). For example, electronics module 70 maytrigger device 30 to perform measurements in response to signals from apressure sensor indicating that the device (or portion thereof, such ascollector 35) is disposed at a predetermined depth underwater. Asanother example, electronics module 70 may trigger sensor 40 to performmeasurements in response to signals from a tilt sensor (e.g., an IMU)indicating a predetermined orientation of collector 35 or othercomponents of the device. The predetermined orientation may, forexample, enable measurements of sky radiance at a desired angle relativeto the horizon and/or to the sun. As yet another example, signals from aclock or a GPS receiver may be used to trigger measurements at desiredtimes and/or locations.

In some examples, two or more criteria are associated with theinformation sensed by a single auxiliary sensor. For example,electronics module 70 may perform an action (e.g., trigger a reading,vary a sampling rate and/or integration time of sensor 40, etc.) basedboth on the actual value of data acquired by the auxiliary sensor and onthe rate at which the data changes with time. For example, ifinformation sensed by a depth sensor indicates that the depth of device30 below water is at least a predetermined amount and is also changingrapidly, electronics module 70 may increase the rate at which the systemacquires data.

In some examples, auxiliary sensors 50 include at least one photodiode(or other suitable device), and electronics module 70 is configured toturn off power to sensor 40, at least some auxiliary sensors 50, and/orother components of device 30 in response to data from the photodiodeindicating that light levels are below a predetermined threshold. Thepredetermined threshold may correspond to light levels associated with atime of day (e.g., night-time), with a specified depth underwater,and/or with a specified tilt angle. Electronics module 70 may restorepower in response to information from the photodiode indicating thatlight levels are above the predetermined threshold. Shutting down powerbased on information sensed by the photodiode may reduce total powerconsumption of device 30 during deployment, and may thereby extend thelength of deployment of an autonomous and/or manually operated device.

Additionally, or alternatively, electronics module 70 may power downcomponents of device 30 in response to information from a real-timeclock. For example, electronics module 70 may shut down sensor 40 andauxiliary sensors 50 when the real-time clock indicates that it is nighttime, and may turn on power to sensor 40 and auxiliary sensors 50 whenthe real-time clock indicates that it is morning.

In some examples, triggering may be additionally or alternatively basedon signals from sensor 40. For example, electronics module 70 may varythe sampling rate or integration time of sensor 40 based on theintensity of light recently measured by the sensor (e.g., across theentire spectrum measurable by sensor 40 or across any suitableportion(s) of the measurable spectrum). In this way, device 30 mayautonomously adapt to, e.g., changing amounts of incident light. Forexample, electronics module 70 may change settings of sensor 40 tosettings suitable for low amounts of light when the measured intensityis low, and, in response to an increase in measured intensity, changethe settings to be suitable for larger amounts of light.

Device 30 further includes an input/output hub 80 allowing forcommunication of data between device 30 and other devices (e.g.,computers, mobile devices, servers, and/or the like). For example,input/output hub 80 may include one or more interface ports such asserial ports, parallel ports, and/or universal serial bus (USB) ports.The interface ports may include dedicated input ports, dedicated outputports, and/or ports capable of both input and output. For example, a USBport may be used to provide input to device 30 and to output informationfrom the sensing system to an external computer or other externaldevice. In some examples, input/output hub 80 includes a wirelesscommunication circuit, which may transmit and/or receive informationusing, e.g., a WiFi wireless technology protocol, a Bluetooth® wirelesstechnology protocol, an Iridium® wireless communications system, and/orthe like. Input/output hub 80 may be used to retrieve data from device30 (e.g., from memory store 75, from a buffer within electronics module70, and/or directly from sensor 40). For example, data may betransferred via input/output hub 80 to an external computer for storageand/or analysis. Input/output hub 80 may additionally or alternativelybe used to input instructions related to the operation of device 30. Forexample, a user may use an external computer and/or mobile devicecoupled to input/output hub 80 to select a parameter of sensor 40 (e.g.,integration time, sampling rate, and/or the like). The selectedparameter is communicated via input/output hub 80 to sensor 40 and/or toelectronics module 70.

In some examples, device 30 includes one or more data processing systems(e.g., computers), such as a single-board Linux computer and/or othersuitable system. The data processing system may communicate with, and/orbe part of, electronics module 70 and/or input/output hub 80. The dataprocessing system may facilitate transfer of data between device 30 andan external system. In some examples, the data processing system remainsin a low-power standby mode until woken by electronics module 70 (e.g.,by one or more microcontrollers of the electronics module) for datatransfer and/or any other suitable purpose. In this manner, power isconserved by keeping the computer in standby mode during much of thetime the device is deployed.

Device 30 further includes a power source 90. Power source 90 mayinclude any suitable battery or batteries, rechargeable or otherwise,such as a lead-acid battery, a lithium-ion battery, a lithium-polymerbattery, a nickel-cadmium battery, and/or the like. The battery may be asecondary cell in communication with a battery-charging circuitconfigured to receive power from an external source and convert thereceived power into an electrical current usable to charge the battery(e.g., a DC current). The charging circuit may be configured to chargethe battery by inductive charging, infrared power transmission,radio-frequency power transmission, and/or by drawing power from anotherdevice via a USB interface port. Power source 90 provides power tocomponents of device 30 having need of a power supply (e.g., needing avoltage or current source). For example, power source 90 may supplyelectrical power to sensor 40, any motorized components of collector 35,input/output hub 80, electronics module 70, light source 60, and/orauxiliary sensors 50. In some examples, power source 90 comprises aphotovoltaic device (e.g., one or more photovoltaic panels, and/or anyother suitable solar power device).

Typically, device 30 includes an enclosure or housing 95 configured toat least partially contain the components described above. Housing 95 isgenerally designed to be suitable for deployment in rough environments(e.g., outdoors, on ships and/or buoys, on aerial vehicles, etc.). Forexample, the housing may be water-resistant or water-tight, and may beconfigured to resist corrosion. In some examples, housing 95 isconfigured to protect its contents when deployed underwater.

B. Illustrative Compact Spectrometer

With reference to FIG. 3, this section describes an illustrative compactspectrometer 100. Compact spectrometer 100 is an example of sensor 40suitable for use in a hyperspectral sensing system in accordance withaspects of the present teachings, as described above. Accordingly,compact spectrometer 100 is configured to measure an intensity ofimpinging light in each of a plurality of adjacent narrow spectralbands. Compact spectrometer 100 may be referred to as amicrospectrometer, and/or an ultra-compact spectrometer.

FIG. 3 is a schematic depiction of compact spectrometer 100. As shown inFIG. 3, compact spectrometer 100 includes an input slit 110 throughwhich incident light enters.

The incident light is typically light emitted by, reflected by,scattered from, and/or transmitted through sample objects within thefield of view of collector 35. Collector 35 directs the incident lightthrough input slit 110, which may include imaging the sample objectsonto input slit 110.

The incident light is transmitted through a hollow space in compactspectrometer 100 and impinges on grating 115. Grating 115 is adiffraction grating comprising an array of fine, parallel grooves on acurved reflective substrate. In the example depicted in FIG. 3, grating115 comprises a reflective concave blazed grating, having grooves shapedto form right triangles, but any suitable type of grating may be used(e.g., a holographic grating, a ruled grating, an echelle grating,and/or any other suitable grating). In some examples, a differentdispersive element (e.g., a transmissive grating and/or a prism) is usedinstead of grating 115.

In general, light impinging on grating 115 is reflected from the gratingin a propagation direction determined at least partially by thewavelength of the light. In other words, grating 115 disperses orseparates the incident light into a plurality of chromatic components(e.g., spectral component or colors), and the chromatic components arereflected from the grating in a wavelength-dependent manner.

The curvature of grating 115, which is concave toward input slit 110,focuses the reflected incident light toward an image sensor 120. Imagesensor 120 is configured to measure an amount (e.g., an intensity) ofthe dispersed and reflected incident light from grating 115 at each of aplurality of spatial positions and/or pixels of the image sensor.Because grating 115 reflects light in a wavelength-dependent direction,the location on image sensor 120 at which light was measured correspondsto the wavelength of the light. Image sensor 120 may comprise a linearCMOS array (e.g., a CMOS device comprising a row of detecting pixels), aCCD array, and/or any other suitable image-sensing device. In someexamples, grating 115 and/or image sensor 120 comprise optoelectronicchips.

Compact spectrometer 100 has a high sensitivity to light, enabling it toacquire hyperspectral data in low-light environments, such asunderwater. Compact spectrometer 100 also has a relatively shortelectro-optical integration time (e.g., it can complete a measurement ina short amount of time). As a result of its high sensitivity and shortintegration time, compact spectrometer 100 has a wide dynamic range. Inother words, it is capable of measuring both low levels and high levelsof light. The wide dynamic range of compact spectrometer 100 enablesdevice 30 to be used in dark underwater environments as well as brightabove-water environments.

Due to its small size, compact spectrometer 100 has a short optical pathlength. That is, light travels a relatively small distance withincompact spectrometer 100. The short optical path length allows for highstability against vibrations, thermal changes, optical defects, straylight, and/or other disruptions.

Suitable examples of compact spectrometer 100 include the compactspectrometer currently sold under the name “C12880MA” by HamamatsuPhotonics and the Carl Zeiss Spectroscopy GmbH product named “MonolithicMiniature Spectrometer, MMS UV-VIS”. Other suitable examples of compactspectrometer 100 may include a spectrometer that supports a spectralresponse up to 850 nm or more (e.g., a spectral domain of 300 nm to 900nm, of 330 nm to 850 nm, and/or any other suitable range), has a maximumspectral resolution of 15 nm, is sized approximately 20×13×10 mm, and/orweighs approximately 5 grams or less. Larger size sensor modules orpackages with sizes approximately 70×60×40 mm and weighing 50 grams orless may be suitable also.

C. Illustrative Devices Having a Light Source

As shown in FIGS. 4-6, this section describes illustrative hyperspectralsensing devices including light sources (e.g., LEDs, lamps, and/orlasers) and configured to illuminate a sample so that scattering,fluorescence, and/or absorption properties of the sample may be probed.These devices may be substantially similar to device 30 in at least somerespects. The sample may be, e.g., a sample of air or a sample of water.In some embodiments, the sample is a defined volume of gas, liquid, orsolid matter inside a container.

FIG. 4 schematically depicts a device 130 configured to illuminate adiscrete and/or flow-through sample. For example, the sample may be adiscrete sample removed from the sampling site (such as a definedquantity of water removed from a body of water using, e.g., a profilingrosette, bottle, and/or other suitable device). Alternatively, oradditionally, the sample may be a flow-through sample configured to bepassed through the viewable region of the sensing system (for example,water may be pumped from a body of water through a sampling chamberdisposed in front of the optical collector, so that a measurement orseries of measurements of the flowing water may be made).

Device 130 includes a light source 136, which comprises one or moredevices configured to emit light, such as LEDs, OLEDs, diode lasers,fiber lasers, lamps, and/or the like. In some examples, the one or morelight-emitting devices of light source 136 each produce lightsubstantially at a single wavelength. For example, LEDs producing lightat 405 nm, 470 nm, 560 nm, and/or 650 nm may be used. In some examples,one or more of the light-emitting devices produce light at a range ofwavelengths. For example, light source 136 may include broadband LEDs(e.g., superluminescent LEDs), a continuum and/or supercontinuum source,and so on. The wavelength range of light source 136 may be selectedbased on specific absorbing, scattering, and/or fluorescing substancesexpected to be present in the sample. Light source 136 may be modulated(e.g., amplitude-modulated and/or phase-modulated) such that light fromthe light source may be distinguished and/or isolated from backgroundand/or ambient light (with or without lock-in amplification).

Device 130 includes an optical assembly configured to prepare lightproduced by the source for transmission to the sample. Typically, theoptical assembly includes a diffuser 138 configured to homogenize (e.g.,diffuse) light produced by the light source, and a lens 139 configuredto collimate light produced by the light source (e.g., totelecentrically illuminate the sample). These components preserve thefocal plane of the light source across the sample volume.

In some examples, light source 136 and/or the associated opticalassembly may be mounted detachably to device 130 so that they can beremoved when not needed, or swapped out for different components.

Device 130 includes an optical collector 142 configured to collect lightfrom the sample and to transfer it to a sensor 146 (e.g., a compactspectrometer or other suitable detector having sufficient spectralresolution for hyperspectral measurements). Collector 142 may includeone or more bandpass and/or notch optical filters configured to blocklight produced by light source 136 that is transmitted through thesample, or passes around the sample, substantially without interactingwith the sample. These filters may, for example, increase asignal-to-noise ratio of the hyperspectral measurement, and/or mayprevent a weak fluorescence signal received at sensor 146 from beingoverwhelmed by a strong background signal from light source 136.

In some examples, one or more optical components of collector 142 areselected based on optical properties associated with a bottle or otherdevice containing the sample (e.g., based on an amount of refractionexperienced by light passing through the bottle). Device 130 may beconfigured to be used with any one of a plurality of interchangeablecollectors having different optical components.

FIG. 5 schematically depicts an illustrative device 150 configured toacquire hyperspectral data using a light source while immersed in asample. For example, device 150 may be immersed in water, may be used tomeasure ambient air, and/or may be used in any other situation whereinthe entire optical path between the light source and the opticalcollector is occupied by the sample. Device 150 includes a light source156, a light-source diffuser 158, a light-source collimating lens 159,an optical collector 162, and a sensor 166.

FIG. 6 schematically depicts an illustrative device 180 configured forselectively adjusting an angle formed by a light source 182 relative toa collector 184 and a sensor 186. Collector 184 and sensor 186 aremounted slidably on a rail 190, as shown in FIG. 6. Typically, collector184 and sensor 186 are connected rigidly, such that they move togetheralong the rail, but other configurations are possible. In a firstposition, the collector and sensor are disposed such that light fromlight source 182 passes through a sample, and light transmitted directlythrough the sample (e.g., without deflection) enters the collector. Inthe first position, collector 184 and sensor 186 face light source 182(e.g., forming a substantially 180-degree angle with the light source)with the sample disposed between them.

With collector 184 and sensor 186 in the first position, the spectralproperties of light striking the sensor can be measured and comparedwith known spectral properties of light source 182, and absorbanceproperties of the sample may be inferred. For example, a measurementindicating that the sample preferentially absorbs light at a certainwavelength may indicate that the sample contains a substance known toabsorb light at that wavelength. This type of measurement may bereferred to as an attenuation measurement, because thewavelength-dependence of the attenuation of light within the sample isprobed.

In a second position, collector 184 and sensor 186 are disposed at anangle less than 180° relative to light source 182. In some cases, theangle may be less than 90°. In the second position, device 180 maymeasure spectral properties of light scattered from the sample. Forexample, sensor 186 may detect light produced by photons from lightsource 182 undergoing Rayleigh scattering, Raman scattering, Brillouinscattering, and/or diffuse reflection from the sample. Additionally, oralternatively, sensor 186 may detect light produced by photons fromlight source 182 inducing fluorescence in the sample, and/or in aconstituent of the sample. The spectrum of light detected at one or moreselected angles may be used to determine concentrations of specificsubstances within the sample (e.g., concentrations of algae withinwater). In some examples, hyperspectral measurements of fluorescence mayindicate the size of particulates within the sample, a concentration ofa specific substance within the sample, a type of substance present inthe sample, and/or a physiological state of a biological species withinthe sample.

Collector 184 and/or sensor 186 may transition between the first andsecond positions by sliding along rail 190. Additional positions alongrail 190 may be possible, e.g., at angles between 0° and 180° relativeto light source 182. In some examples, the variation of the samplespectrum in response to changes in the angle subtended by the collectoroptical axis and the angle of the excitation source may be probed. Theangular dependence of the spectrum may indicate Rayleigh scattering inthe sample and therefore may be used to detect a presence or abundanceof small particles (e.g., particles <0.5 microns in size).

In some examples, collector 184 and sensor 186 are moved by an actuator,and rail 190 is a fixed armature of the actuator. The actuator mayenable the collector and sensor to be positioned with high accuracy andprecision for reproducible angularly-resolved measurements. In someexamples, light source 182 slides along the rail while collector 184 andsensor 186 are fixed in place.

D. Illustrative Collectors for Simultaneous Measurement

With reference to FIGS. 7-14, this section describes illustrativeoptical collectors for use in simultaneous hyperspectral measurements oflight propagating from two different directions using a single sensor(e.g., a single spectrometer). In some examples, light from onedirection is propagating from the sky, and light from the otherdirection is propagating from a body of water. Simultaneous sky-watermeasurements may, for example, be used to determine spectral propertiesof a body of water such as an ocean, coastal region, lake, reservoir,estuary, river, and/or the like. For convenience, collectors configuredfor use in measurement of light from two directions are described hereinin the context of simultaneous sky and water radiance measurements.However, generally, light from the two directions may have any suitableorigin.

In some examples, a body of water may be characterized by a spectralremote-sensing reflectance, e.g., a wavelength-dependent ratio ofradiance leaving the water in a particular viewing direction to thetotal sky irradiance just above the water's surface. The remote-sensingreflectance R_(rs) may be defined by the following equation:

${R_{rs}( {\theta,\phi,\lambda} )} = \frac{L_{w}( {\theta,\phi,\lambda} )}{E_{d}(\lambda)}$

where L_(w)(θ,φ,λ) is the radiance of water-leaving light at wavelengthλ in a direction defined by polar and azimuthal angles (θ,φ) andE_(d)(λ) is the irradiance of downwelling light at wavelength λ incidenton the water surface. Downwelling light typically includes light fromthe sky. Water-leaving light includes light emerging from beneath thesurface of the water, such as light from the sky that traveled beneaththe surface and was scattered upward through the surface. Water-leavinglight by definition does not include light reflected directly from thesurface of the water substantially without traveling underwater. Ingeneral, a direct measurement of the water-leaving radiance is notpossible because a detector pointed at the water surface will measurethe total upwelling radiance, which includes both the water-leavingradiance and the surface-reflected radiance. That is, a detectormeasures

L _(T)(θ,φ,λ)=L _(w)(θ,φλ)+L _(r)(θ,φ,λ)

where L_(T)(θ,φ,λ) is the total upwelling radiance and L_(r)(θ,φ,λ) isthe surface-reflected radiance. The water-leaving radiance may, however,be estimated from the total upwelling radiance by several methods. Insome methods, the surface-reflected radiance is estimated by multiplyingthe sky radiance by a correction factor. For example,

L _(r)(θ,φ,λ)=ρL _(s)(θ′,φ′,λ)

where the angles (θ′, φ′) denote a direction within the field of view ofthe detector when the detector is pointed in a direction suitable forsampling the sky radiance that would specularly reflect from the watersurface into the direction defined by (θ, φ). The correction factor ρ,which may be a Mobley surface correction, may depend on either or bothangles (θ, φ), wavelength λ, and/or other factors such as environmentalfactors. Using this estimate for the surface-reflected radiance, theremote-sensing reflectance may be estimated as

${R_{rs}( {\theta,\phi,\lambda} )} = {\frac{{L_{T}( {\theta,\phi,\lambda} )} - {\rho \; {L_{s}( {\theta^{\prime},\phi^{\prime},\lambda} )}}}{E_{d}(\lambda)}.}$

Alternatively, or additionally, the surface correction of the skyradiance can be explicitly modeled using a bidirectional reflectancedistribution function (BRDF) using the known imaging view geometry(e.g., solar zenith and azimuth angles, and sensor zenith and azimuthview angles), a model of the angle and/or wavelength-dependentreflectance at the air-water interface, and the interface roughness dueto wave facets on the water surface. Alternatively, or additionally, aplurality of sensors may measure the total upwelling radiance fromdifferent angles, and the surface correction may be obtained based onthe measurements. In these examples, measuring the sky radiance isoptional.

The downwelling irradiance E_(d)(λ) may be estimated by measuring thereflectance of a reference surface having a known reflectance parameterR_(ref). A typical reference surface is a flat, rigid plaque configuredto reflect incident light diffusely and isotropically (e.g., aLambertian reflector). For example, a card coated with barium sulfateand/or magnesium oxide may be used as a plaque. The radiance L_(ref) oflight reflected by the plaque is independent of the measurement angleand may be calculated as

${L_{ref}(\lambda)} = {( \frac{R_{ref}(\lambda)}{\pi} ){{E_{d}(\lambda)}.}}$

The remote-sensing reflectance for a given wavelength in a givendirection may be calculated from the measured total upwelling radiance,the measured sky radiance, and the measured reference plaque reference:

$R_{rs} = {\frac{( {L_{T} - {\rho \; L_{s}}} )}{\pi ( {L_{ref}/R_{ref}} )}.}$

In other examples, the downwelling irradiance may be measured directly.

In known systems for remote-sensing radiance measurements, the skyradiance and the total upwelling radiance are typically measured insequence with a single detector or simultaneously with multiplerespective detectors. (The reference plaque, if used, may be measured atthe same time as the total upwelling radiance, or may be measuredindependently.) Both sequential measurements and multiple-detectormeasurements have disadvantages. Environmental conditions may changesignificantly between sequential measurements, leading to uncertaintyand/or noise in the measured data. Data acquired by multiple detectorsmay include errors due to the detectors having different sensitivities,being imperfectly calibrated, and/or triggering data acquisition atslightly different times. Systems and methods of the present disclosureallow simultaneous measurement of the total upwelling radiance, skyradiance, and optionally a reference plaque radiance, using a singlehyperspectral-capable sensor (e.g., a single compact spectrometer).

Illustrative sky-water collectors configured to collect light for asimultaneous sky-water measurement are described below. As definedherein, a simultaneous sky-water measurement is a substantiallysimultaneous measurement of total upwelling radiance and sky radiance,and may include a substantially simultaneous or a non-simultaneousmeasurement of a reference plaque. An illustrative sky-water collector250 is depicted schematically in FIG. 7. Sky-water collector 250includes a sky-radiance aperture 254 configured to allow light to enterthe collector, a water-radiance aperture 256 configured to allow lightto enter the collector, and an optical director 258 (e.g., an assemblyof optical components) configured to direct light entering through thesky-radiance aperture and light entering through the water-radianceaperture toward a sensor within the device. Sky-radiance aperture 254and water-radiance aperture 256 may include slits of fixed or variablewidth, optical diffusers, lenses, and/or other optical components.Optionally, sky-water collector 250 may include steering optics (e.g.,mirrors, filters, polarizers, polarizing and/or nonpolarizingbeamsplitters, and/or other steering components) configured to directlight in one or more directions, refractive and/or diffractive elementsconfigured to adjust beam sizes and/or shapes of light, beam blocksconfigured to block light from entering one or more portions of thecollector, and/or modulators configured to modulate light (e.g., tomodulate a phase and/or amplitude of light).

Optionally, a modulator 259 may be disposed between sky-radianceaperture 254 and optical director 258 and configured to modulate lightentering through sky-radiance aperture 254, such that the portions oflight entering the collector through that aperture may be distinguished.Additionally, or alternatively, a modulator may be disposed betweenwater-radiance aperture 256 and optical director 258.

Modulator 259 may comprise any suitable system or device configured tomodulate light. In some examples, modulator 259 includes a scanningoptical element configured to periodically deflect light such that atleast a portion of the light deviates from the optical path it wouldotherwise travel. For example, a scanning mirror may periodicallydeflect the light such that the light is not directed toward a sensor,or such that the light strikes a beam block within sky-water collector250.

Additionally, or alternatively, modulator 259 may comprise a chopperwheel 260. Chopper wheel 260, depicted in FIG. 8, includes an opaquesubstrate (e.g., a disc) having openings 261 at regular angularintervals. Adjacent openings 261 are separated by unmodified blockingportions 262 of the opaque substrate. Chopper wheel 260 is rotatablymounted to a support and configured to rotate at a fixed, stable rate(e.g., a motor may drive the chopper wheel to rotate at a specificspeed). Light may be amplitude-modulated by placing chopper wheel 260 inthe optical path of the light. As chopper wheel 260 rotates, the lightis periodically blocked by blocking portions 262 and allowed to pass byintervening openings 261. The periodic blocking effectively modulatesthe amplitude of the light.

In examples including modulation of light entering through one or moreapertures, an electronics module (e.g., electronics module 70) may beconfigured to trigger data acquisition by the sensor in phase (andfrequency) with the modulation cycle of the modulator. For example, dataacquisition may be triggered in phase with the blocking of the incominglight by chopper wheel 260, or in phase with the passing of the lightthrough openings 261 of chopper wheel 260. In this way, data acquired bythe sensor when one or more apertures are blocked by chopper wheel 260may be compared with data acquired when no apertures are blocked bychopper wheel 260, and so the contribution of light from each aperturemay be identified. In some examples, a lock-in amplifier is used toselectively amplify the modulated signal and reject any signals that donot vary with the phase and/or frequency associated with the modulator.

Although sky-water collectors disclosed herein are primarily describedas enabling measurement of light from sky and from water simultaneously,they may be used to simultaneously measure light from any two suitablesources. For example, a sky-water collector may be included in ahyperspectral sensing device deployed underwater, and the sky-watercollector may collect light originating near the floor of the body ofwater and light originating near the surface of the body of water. Inthis manner, the hyperspectral sensing device may simultaneously measureradiances of floor light and surface light.

Illustrative examples of sky-water collectors are described below.Various components of the illustrative sky-water collectors describedbelow may be combined in any suitable combination.

i. Illustrative Convex Reflector Collector

An illustrative convex reflector collector 270 is depicted in FIG. 9.Convex reflector collector 270 is an example of sky-water collector 250.Convex reflector collector 270 includes a convex reflector 272, which isan example of optical director 258. Convex reflector 272 is a curvedreflective element configured to direct light entering convex reflectorcollector 270 toward the sensor. Convex reflector 272 may focus light ata primary focus point between the convex reflector and the sensor.Sky-radiance aperture 254, water-radiance aperture 256, and convexreflector 272 are disposed such that light entering through thewater-radiance aperture and light entering through the sky-radianceaperture reflect from the convex reflector along substantially paralleloptical paths and/or substantially overlapping optical paths (e.g.,light from the two apertures may co-propagate after reflecting from theconvex reflector). Modulator 259 may be disposed adjacent one of theapertures such that light entering the aperture is modulated prior toreflecting from convex reflector 272. In the example depicted in FIG. 9,modulator 259 is disposed exterior to sky-radiance aperture 254, butmodulators may additionally or alternatively be disposed interior to thesky-radiance aperture, and/or exterior and/or interior to water-radianceaperture 256. For example, modulator 259 may be disposed interior to thesky-radiance aperture, such that modulator 259 is disposed between thesky-radiance aperture and convex reflector 272.

ii. Illustrative Movable Reflector Collector

An illustrative movable reflector collector 280 is depicted in FIG. 10.Movable reflector collector 280 is an example of sky-water collector250. Movable reflector collector 280 includes a movable reflector 282,which is an example of optical director 258. Movable reflector 282comprises a reflective optical element mounted rotatably within movablereflector collector 280. Movable reflector 282 may additionally oralternatively be mounted translatably within movable reflector collector280. Movable reflector 282, sky-radiance aperture 254, andwater-radiance aperture 256 are disposed within movable reflectorcollector 280 such that positioning the movable reflector at a firstposition (e.g., a first orientation) causes light entering from thesky-radiance aperture to be reflected toward sensor 40 and lightentering from the water-radiance aperture to be reflected away from thesensor, and positioning the movable reflector at a second positioncauses light entering from the sky-radiance aperture to be reflectedaway from the sensor and light entering from the water-radiance apertureto be reflected toward the sensor. The position of movable reflector 282may be adjusted very quickly to switch between measurements of skyradiance and water radiance, and no other component of movable reflectorcollector 280 requires adjustment to switch from sky to water radiancemeasurements. Therefore, movable reflector collector 280 may bedescribed as capable of substantially simultaneous measurements of skyand water radiance.

One or more beam blocks 284 configured to substantially preventtransmission and specular reflection of impinging light may be disposedwithin movable reflector collector 280 to block light that is notdirected toward the sensor. Use of beam blocks 284 may reduce straylight reaching the sensor and thereby decrease noise and uncertainty inthe hyperspectral measurement. Movable reflector collector 280 mayinclude one or more modulators 259.

iii. Illustrative Movable Disperser Collector

An illustrative movable disperser collector 290 is depicted in FIG. 11.Movable disperser collector 290 is an example of sky-water collector250. Movable disperser collector 290 includes a movable dispersingelement 292, which is an example of optical director 258. Movabledispersing element 292 is a dispersing optical element (e.g., a prism,grating, and/or any other suitable element configured to direct light ina wavelength-dependent direction) mounted rotatably within movabledisperser collector 290, and may additionally or alternatively bemounted translatably within the collector. Movable dispersing element292, sky-radiance aperture 254, and water-radiance aperture 256 aredisposed within movable disperser collector 290 such that positioningthe movable dispersing element at a first position (e.g., a firstorientation) causes light entering from the sky-radiance aperture to bereflected toward the sensor and light entering from the water-radianceaperture to be reflected away from the sensor, and positioning themovable dispersing element at a second position causes light enteringfrom the sky-radiance aperture to be reflected away from the sensor andlight entering from the water-radiance aperture to be reflected towardthe sensor. Beam blocks 284 may be disposed within movable dispersercollector 290 to block stray light from the detector. Modulator 259 maybe included. Because movable dispersing element 292 spatially separateslight according to wavelength, dispersing elements typically included inthe sensor may be omitted when movable disperser collector 290 is used.For example, the sensor may comprise replaced by a linear CMOS or CCDarray rather than a spectrometer.

iv. Illustrative Beamsplitter Collector

An illustrative beamsplitter collector 300 is depicted in FIG. 12.Beamsplitter collector 300 is an example of sky-water collector 250.Beamsplitter collector 300 includes a beamsplitter 302 configured tocombine light impinging on the beamsplitter from two or more directionsinto a single copropagating beam of light. Typically, beamsplitter 302is configured to reflect a portion of incoming light and to transmit aportion of incoming light. Beamsplitter 302 may comprise a partiallyreflecting mirror, a beamsplitter cube, a fiber-optic beamsplitter, ahalf-silvered mirror, a pellicle (e.g., a thin membrane), a waveguidebeamsplitter, and/or a micro-optic beam splitter. Beamsplitter 302,sky-radiance aperture 254, and water-radiance aperture 256 are disposedwithin beamsplitter collector 300 such that light from the sky-radianceaperture and light from the water-radiance aperture impinge on thebeamsplitter from different directions and are emitted by thebeamsplitter in the same direction toward the sensor. One or moretransmissive polarizing elements (e.g. a wire-grid polarizer, aquarter-wave plate, etc.) and/or reflective polarizing elements (e.g., athin film, another beamsplitter, etc.) may be disposed betweenbeamsplitter 302 and sky-radiance aperture 254, and/or betweenbeamsplitter 302 and water-radiance aperture 256, to polarize the light.The polarizing elements may polarize the light linearly, circularly,and/or elliptically. Beamsplitter 302 may be a polarizing beamsplitter,and the polarizing elements may be configured to polarize the sky lightand/or water light such that substantially all of the sky light andwater light is combined at the beamsplitter and directed toward thesensor. Additionally, beam blocks, mirrors and/or other suitablereflectors, and/or modulators may be included as needed.

v. Illustrative Sky-Water-Plaque Collector

An illustrative sky-water-plaque collector 310 is depicted in FIG. 13.Sky-water-plaque collector 310 is configured to collect light from thesky, from the water, and from a reference plaque substantiallysimultaneously. Sky-water-plaque collector 310 includes a first opticalassembly 311 configured to combine two sources of light (e.g., from thesky and from the plaque, from the sky and from the water, or from thewater and from the plaque) into a first light combination, and totransmit the first light combination to a second optical assembly 312configured to combine the first light combination with a third source oflight (e.g., light from a source not included in the first lightcombination) to produce a second light combination including light fromall three sources. In the example depicted in FIG. 13, the first andsecond optical assemblies comprise beamsplitters, but any suitablecombination of optical elements may be included. One or more polarizingelements 315 may be included to polarize light such that it is reflectedby, or transmitted by, a polarizing beamsplitter in first opticalassembly 311. Additionally, or alternatively, polarizing elements may beconfigured to polarize light such that it is reflected or transmitted bysecond optical assembly 312, and/or any other suitable opticalcomponent(s). Mirrors and/or polarizing reflectors may be used to steerthe first light combination toward second optical assembly 312 and/or tosteer the second light combination toward the sensor. Any one or more ofthe sky light, plaque light, and water light may be independentlymodulated with a modulation depth, frequency, and/or pattern configuredto enable the light from the different sources to be distinguished fromeach other.

vi. Illustrative Sequential Plaque Collector

An illustrative sequential plaque collector 320 is depicted in FIG. 14.Sequential plaque collector 320 includes a sky-water collector 250(e.g., beamsplitter collector 300) and further includes an alternatingelement 322 configured to selectively direct sky light or plaque lightinto an entrance aperture of the collector. For example, alternatingelement 322 may include a reference plaque and a mirror, either or bothof which are movable. The mirror may be moved in front of the plaquesuch that it substantially blocks light reflected by the plaque fromentering the entrance aperture and reflects sky light into the entranceaperture, or moved away from the plaque such that light reflected by theplaque may enter the aperture. The mirror and/or the plaque may bedisposed on translation stages that are manually movable and/ormotorized. The mirror and/or the plaque may be mounted rotatably, suchthat the mirror may be rotated in front of the plaque for a sky radiancemeasurement and rotated away from the plaque for a plaque radiancemeasurement. Water light enters sequential plaque collector 320 througha second entrance aperture (e.g., water-radiance aperture 256)regardless of the position of alternating element 322. A simultaneousmeasurement of water-radiance and sky-radiance may therefore be followedimmediately by a simultaneous measurement of water-radiance andplaque-radiance. The time between the first simultaneous measurement andthe second simultaneous measurement is so short (due to the speed atwhich alternating element 322 may be moved, as well as timecharacteristics of the associated sensor) that the first and secondmeasurements may be considered nearly simultaneous with each other formany purposes. For example, atmospheric conditions such as cloud coverare likely to change very little between the first and secondmeasurements.

In the example depicted in FIG. 14, alternating element 322 alternatelyallows sky light or plaque light to enter a first aperture, while waterlight is never prevented from entering a second aperture. In otherexamples, alternating element 322 alternately allows water light orplaque light to enter the first aperture, while sky light is neverprevented from entering the second aperture.

Alternating element 322 may be added to any suitable sky-water collector250 (e.g., exterior to the sky-radiance aperture 254 or water radianceaperture 256) to make a sequential plaque collector

E. Illustrative Network of Hyperspectral Sensing Devices

With reference to FIG. 15, this section describes an illustrativehyperspectral sensing system 350 in accordance with aspects of thepresent teachings.

As depicted schematically in FIG. 15, system 350 comprises a pluralityof hyperspectral sensing devices 355. Devices 355 may comprise and/or besimilar to device 30 and/or any other suitable devices. Devices 355 maybe distributed across any suitable area. In some examples, devices 355are distributed adjacent to, on, and/or in a body of water (e.g., abovewater, underwater, on coasts, on buoys, on weather stations, and/or inany similar location). Additionally, or alternatively, devices 355 maybe distributed on land, on aerial vehicles, on weather balloons, and/orin any suitable location. Each device 355 is configured to acquirehyperspectral data and to transmit the acquired data to a locationremote from the device. Although a selected number of devices is shownin FIG. 15, more or fewer devices may be utilized, and system 350 mayinclude different numbers of devices at different times. In someexamples, subsets of devices within the system may be collocated.

Each device 355 is configured to communicate with a computer network 360via a wireless communications module of the device (e.g., input/outputhub 80, described above). Computer network 360 may be configured tocommunicate data (e.g., to transmit data to and/or receive data from) aserver 365 and/or any other suitable device. Communication betweendevices 355 and computer network 360, and/or between the network andserver 365, may comprise radio frequency (RF) antenna, GSM (GlobalSystem for Mobile Communications)/GPRS (General Packet Radio Service)cellular modem, Bluetooth® wireless technology, WiFi, and/or any othersuitable protocol.

Typically, devices 355 are configured for autonomous operation. In thismode, the devices transmit acquired data to server 365 via computernetwork 360 at selected intervals (e.g., on a predetermined schedule, inresponse to the acquired data meeting one or more criteria, and/or atany other suitable time). For example, data may be transmitted daily,several times per day, in real-time or near real-time (e.g.,approximately every 10-15 minutes), and/or at any other suitableinterval.

In some examples, server 365 transmits data and/or instructions to oneor more of the devices (e.g., to an input/output hub of a device). Thesecommunications from server 365 may include software and/or firmwareupdates, operating settings (e.g., sample rates, sample intervals,integration times, types of data, etc.), calibration routines, and/ordata transfer protocols of the devices. Additionally, or alternatively,server 365 may synchronize clocks of the devices, manage power settingsof the devices, and/or trigger specific routines (e.g., calibration).

In some examples, devices 355 are each configured to communicate withone or more of a plurality of different servers using one or moreparallel networks.

In examples wherein each device 355 includes two or more sensors (e.g.,two or more spectrometers, or at least one spectrometer and at least oneauxiliary sensor), any suitable electronics on board the device may beused to operate the sensors and to transmit the acquired data. Forexample, the device may include a dedicated microcontroller or otherprocessing logic for each of the sensors. Alternatively, a singlemicrocontroller may be configured to control all sensors, or a pluralityof the sensors. In some examples, a data processing system on board thedevice may be configured to control the sensors and/or to controlcommunication with network 360.

In cases wherein data from the two or more sensors is combined, the datamay be transmitted to server 365 independently and combined aftertransmission to reduce power usage of the local electronics module.However, in some cases, data from multiple sensors is combined prior totransmission to reduce transmission bandwidth.

F. Illustrative Method for Sky-Water Measurement

This section describes steps of an illustrative method 400 forsimultaneous or near-simultaneous measurement of a combination of skyradiance, water radiance, and/or plaque radiance; see FIG. 16. Aspectsof sky-water collectors 250 may be utilized in the method stepsdescribed below. Where appropriate, reference may be made to componentsand systems that may be used in carrying out each step. These referencesare for illustration, and are not intended to limit the possible ways ofcarrying out any particular step of the method.

FIG. 16 is a flowchart illustrating steps performed in an illustrativemethod, and may not recite the complete process or all steps of themethod. Although various steps of method 400 are described below anddepicted in FIG. 16, the steps need not necessarily all be performed,and in some cases may be performed simultaneously or in a differentorder than the order shown.

At step 402, the method includes receiving a first portion of light inan optical collector of a hyperspectral sensing system (e.g.,hyperspectral sensing device 30). For example, the first portion oflight may be light from the sky above a body of water. The first portionof light may be received via a first entrance aperture of the opticalcollector.

At step 404, the method optionally includes modulating the first portionof light. For example, the first portion of light may beamplitude-modulated using a chopper wheel.

At step 406, the method includes receiving a second portion of light inthe optical collector, e.g., via a second entrance aperture of theoptical collector. For example, the second portion of light may be lightupwelling from a body of water.

At step 408, the method optionally includes modulating the secondportion of light, e.g., using a chopper wheel.

At step 410, the method optionally includes receiving a third portion oflight in the optical collector. The third portion of light may bereceived via a third entrance aperture of the collector. The thirdportion of light may be light reflected from a reference plaque.

At step 412, the method optionally includes modulating the third portionof light. If more than one of the first, second, and third portions oflight are modulated, they are modulated in different patterns. Forexample, they may be modulated with chopper wheels 260 having openingsof different sizes, having blocking portions of different sizes, and/orhaving different speeds of rotation. The different modulation enablesthe measurements of the first, second, and third light portions to bedistinguished from each other, e.g., as described above with referenceto chopper wheel 260.

At step 414, the method includes directing the first portion of lighttoward a sensor (e.g., toward an entrance aperture of sensor 40 and/ortoward an imaging detector such as a CCD or CMOS array). At step 416,the method includes directing the second portion of light toward thesensor. At step 418, the method optionally includes directing the thirdportion of light, if it is present, toward the sensor. Directing any oneof the portions of light toward the sensor may include reflecting thelight from a reflective optical element, dispersing the light with adispersive optical element, and/or diffracting the light with adiffractive optical element. Directing the portions of light toward thesensor may include combining one or more portions of light using apolarizing or nonpolarizing beamsplitter. Combining one or more portionsof light using a polarizing beamsplitter may include polarizing at leastone of the portions of light (e.g., using a wire-grid polarizer,quarter-wave plate, and/or thin-film polarizer) such that it reflectsfrom the beamsplitter and is combined with at least one portion of lightthat impinges on the beamsplitter from another direction and istransmitted. In some cases, two portions of light may be combined firstand subsequently combined with the third portion of light. In someexamples, directing the first, second, and/or third portion of lighttoward the sensor includes adjusting a position of an optical element,such as a rotatable mirror. In some embodiments, steps 402 and 414 maybe performed simultaneously and/or before step 406.

At step 420, the method includes measuring a spectrum of light impingingon the sensor. The impinging light typically includes the first, second,and/or third portions of light directed toward the sensor in steps414-418. Measuring a spectrum of the light may include using atransducer configured to convert intensity of received light into anelectrical voltage or current signal readable by an electronics module.In some examples, measuring a spectrum of the impinging light mayinclude dispersing the impinging light with a dispersive optical elementsuch as a blazed grating, such that spectral components of the impinginglight are spatially separated. Measuring the spectrum may furtherinclude measuring an intensity of light at a variety of spatial points(e.g., using a CMOS linear array) and converting the spatial points intospectral information.

At step 422, the method optionally includes logging the measured data(e.g., the voltage signals read by the electronics module) in a volatileor nonvolatile storage medium (e.g., a memory) associated with thehyperspectral sensor. Alternatively, or additionally, the measured datamay be transmitted to an external device.

G. Illustrative Method for Hyperspectral Measurements Using a LightSource

This section describes steps of an illustrative method 500 forhyperspectral measurements of absorption, fluorescence, luminescence,and/or scattering; see FIG. 17. Aspects of hyperspectral sensing device30 may be utilized in the method steps described below. Whereappropriate, reference may be made to components and systems that may beused in carrying out each step. These references are for illustration,and are not intended to limit the possible ways of carrying out anyparticular step of the method.

FIG. 17 is a flowchart illustrating steps performed in an illustrativemethod, and may not recite the complete process or all steps of themethod. Although various steps of method 500 are described below anddepicted in FIG. 17, the steps need not necessarily all be performed,and in some cases may be performed simultaneously or in a differentorder than the order shown.

At step 502, the method includes positioning a sample, a light source,and an optical collector of a hyperspectral sensing system. Thecollector may comprise a slit of fixed width, and may further includeoptical components as discussed elsewhere herein. Positioning thesample, the light source and the collector may include immersing thelight source and/or collector in water (e.g., placing the collector andlight source underwater). In this case, the hyperspectral measurementsare made underwater, and the water may be the sample. In some examples,the sample is between the light source and the collector. In someexamples, the light source and the collector form a desired angle withsample, which may be an acute angle (e.g., to enable back-scatteringmeasurements).

In some examples, step 502 includes disposing one or more tagging agentsin the sample (e.g., biologically and/or chemically altering the sampleby addition of the tagging agents). Tagging agents are configured tobind with specific predetermined substances such as biological cells,sub-cellular structures, sub-sub-cellular structures and/or compounds(e.g., proteins), and to exhibit identifiable wavelength-dependentluminescent and fluorescent signatures (e.g., in response toillumination by a suitable light source). The addition of tagging agentsto the sample thus enables the identification of the tagged substances(e.g., a specific cellular species) and/or determination of theabundance, mass, or density of the tagged substance based on theintensity of the fluorescence, the volume of the sample throughout whichthe tagging agents are distributed, and/or any other suitable factors.Suitable tagging agents may include lanthanides and/or any othersuitable reagents.

At step 504, the method includes illuminating the sample with the lightsource. The light source may be an LED, laser, lamp, and/or any othersuitable device configured to emit light. In some examples, the lightsource is modulated.

At step 506, the method includes receiving light propagating from thesample using the collector. The collector may receive light propagatingfrom the sample along a predetermined direction and/or within apredetermined solid angle. Receiving light propagating from the samplemay include receiving light that originated in the light source and wasscattered elastically or inelastically from the sample, was reflectedfrom the sample, and/or was transmitted through the sample, and/or mayinclude receiving light that was produced by fluorescence within thesample (e.g., by substances within the sample, by tagging agents withinthe sample, etc.). The collector may be configured to attenuate and/orblock light received directly from the light source.

At step 508, the method includes measuring a spectrum of the receivedlight. Measuring a spectrum of the received light may include directingthe light toward a sensor, such as a compact spectrometer, using thecollector.

At step 510, the method optionally includes logging the measured data ina memory device of the hyperspectral sensing system.

H. Illustrative Method for Assessing Water Quality

This section describes steps of an illustrative method 600 for assessingwater quality; see FIG. 18. Aspects of hyperspectral sensing systems anddevices described above may be utilized in the method steps describedbelow. Where appropriate, reference may be made to components andsystems that may be used in carrying out each step. These references arefor illustration, and are not intended to limit the possible ways ofcarrying out any particular step of the method.

FIG. 18 is a flowchart illustrating steps performed in an illustrativemethod, and may not recite the complete process or all steps of themethod. Although various steps of method 600 are described below anddepicted in FIG. 18, the steps need not necessarily all be performed,and in some cases may be performed simultaneously or in a differentorder than the order shown.

A step 602, the method includes receiving ambient light through a firstaperture of a housing of an optical device (e.g., device 30) disposedadjacent a surface of a body of water. The first aperture may be, e.g.,the aperture of an optical collector of the device. The first apertureis directed at the surface of the body of water, such that lightreceived through the first aperture includes light reflected from thesurface and light passing through the surface from underneath.

At step 604, the method includes receiving ambient light through asecond aperture of the housing, the second aperture being directed atthe sky. Light received from the second aperture therefore includeslight coming from the sky. The second aperture may be, e.g., a secondaperture of an optical collector of the device, which may be the samecollector that includes the first aperture, or a different collector ofthe device. Receiving light at step 602 and/or step 604 may includemodulating the received light (e.g., to facilitate distinguishingmeasurements of the light received through the different apertures).

At step 606, the method includes directing light received from the firstand second apertures into a sensor assembly disposed within a housing ofthe device. The light may be directed by any suitable optical assembly.In some examples, the sensor assembly includes a first spectrometer forsensing light received through the first aperture and a secondspectrometer for sensing light received through the second aperture.Alternatively, the sensor assembly may comprise only one spectrometer,and light received through either aperture is directed to the samespectrometer. In the latter case, light from the two apertures may bedirected simultaneously or sequentially to the common spectrometer(e.g., using aspects of illustrative sky-water collectors describeabove).

At step 608, the method includes sensing, using the sensing assembly,data corresponding to a spectrum of the light received from the firstand second apertures. The spectrometer(s) of the sensing assembly havesufficient spectral resolution to enable hyperspectral measurements. Thedata may be sensed simultaneously or sequentially.

At step 610, the method optionally includes sensing data correspondingto a spectrum of light reflected by a reference plaque. As described inSection D, a reference plaque typically comprises a flat, rigid surfacehaving known reflectance properties and configured to reflect incidentlight diffusely and isotropically (e.g., a Lambertian reflector).Accordingly, with the reference plaque positioned above the surface ofthe water, a spectrum of light reflected from the sensing plaque may beused to determine wavelength-dependence of the irradiance of downwellinglight from the sky in all directions. This may be used to calculate aremote-sensing reflectance (e.g., a measure of how much of the radiancetraveling in all downward directions is reflected upward into anydirection), and/or any other suitable property. Sensing datacorresponding to the spectrum reflected by the reference plaquetypically includes receiving data light reflected by the referenceplaque through any suitable aperture of the device, including the firstor second apertures, and sensing the spectrum using any suitablespectrometer.

At step 612, the method includes determining, based on the sensed data,a spectrum of light originating underneath the surface of the water(e.g., water-leaving light). For example, a remote-sensing reflectancemay be calculated based on the following equation:

$R_{rs} = {\frac{( {S_{T} - {\rho \; (\theta)S_{s}}} )}{\pi ( {S_{ref}/R_{ref}} )}.}$

In this equation, S_(T) represents a measured signal of light receivedthrough the first aperture (the water-directed aperture), S_(S)represents a measured signal of light received through the secondaperture (the sky-directed aperture), S_(ref) represents an averagemeasured signal from a reference plaque, and R_(ref) represents areflectivity of the plaque. The measured signal from the second apertureis multiplied by ρ(θ), a proportionality factor relating radiancemeasured when the detector views the sky to the reflected sky radiancemeasured when the detector views the sea surface. The value of thisproportionality factor may depend on wind speed and direction, detectorfield of view, and sky radiance distribution. The proportionality factormay be a Mobley proportionality factor. The factor of π converts thereflected plaque radiance to an irradiance (e.g., it convertsdirection-dependent data to direction-independent data). Alternatively,or additionally, calculating the remote-sensing reflectance may includedetermining a surface correction of the radiance received through thesky-directed aperture based on a bidirectional reflectance distributionfunction.

As described above in Section D, the remote-sensing reflectance obtainedby this calculation comprises information about a spectrum of lightoriginating underneath the surface of the water (e.g., the water-leavinglight); specifically, the remote-sensing reflectance is awavelength-dependent ratio of radiance leaving the water in a particularviewing direction to the total sky irradiance just above the water'ssurface. Accordingly, the remote-sensing reflectance may be used toinfer information about, e.g., biological, chemical, and/or geologicalconstituents in the water containing substances (e.g., pigments) thatalter optical properties of the water. In other examples, a spectrum oflight originating underneath the surface of the water may be obtained inanother way.

As described above, in some examples light received through the firstand second apertures are measured by a same spectrometer, possiblysimultaneously. In other words, a signal measured by the spectrometerincludes contributions from light received through the first apertureand contributions from light received through the second aperture. Inthese examples, determining a spectrum of water-leaving light at step612 includes distinguishing the two contributions (e.g., to obtain thesignals S_(T) and S_(S) described above). Typically, distinguishing thetwo contributions is facilitated by modulating the light receivedthrough one or both apertures (e.g., prior to measuring the light withthe spectrometer), as described above with reference to steps 602 and604. As an example, if the light is modulated such that light from oneaperture is periodically blocked (or otherwise prevented from reachingthe spectrometer), the two contributions may be distinguished bysubtracting a spectrum obtained when light from one aperture is blockedfrom a spectrum obtained when neither aperture is blocked. For example,the spectrum of light received through the sky-directed aperture couldbe subtracted from the combined spectrum of light received through bothapertures. Typically, light received through one aperture (usually thesky-directed aperture) has a higher intensity than light receivedthrough the other aperture, and subtracting the higher of the twosignals from the combined signal may increase the accuracy of thecalculation. The subtraction may be performed by hardware and/orsoftware.

Additionally, or alternatively, the contributions from the sky-directedaperture and the water-directed aperture may be separated usingdifferential spectral and/or a filter configured to selectively rejectand/or transmit one or both contributions based on a frequency and/orphase of modulation. In some cases, a lock-in amplifier may be used toisolate one of the contributions, based on the frequency and/or phase ofmodulation. In some cases, light received through one or both aperturesis wavelength-modulated (e.g., using a movable dispersing element) tofacilitate separation of the two signals using a spectral filter. Basedon known properties of the wavelength modulator, the measuredwavelength-modulated spectra can be corrected. These techniques may beimplemented in hardware and/or software.

Any processing performed to distinguish the two contributions may beperformed on board the device or on an external data processing system(or other suitable device).

At step 614, the method optionally includes using the sensed data and/orthe determined spectrum of water-leaving light to update remote-sensingdata of the same body of water obtained by an airborne device.Remote-sensing data (e.g., wavelength-dependent reflectances of groundand/or bodies of water) obtained by an airborne device (e.g., asatellite, aircraft, unmanned aerial vehicle, and/or the like) commonlyincludes inaccuracies due to the passage of the sensed light through theatmosphere. Clouds, turbulence, and/or other aspects of the atmospheremay distort the light as it travels from the ground to the airbornedevice, such that the measured light does not accurately representoptical properties of the ground and/or water being surveyed.Additionally, an airborne device positioned high above the surface ofthe Earth may have a lower temporal, spatial, and/or spectral resolutionthan an instrument disposed nearer the ground. Accordingly, theremote-sensing data obtained by the airborne device may becross-referenced with the spectrum obtained at step 612 to correctinaccuracies in the remote-sensing data. Corrections obtained in thismanner may be extrapolated to remote-sensing data obtained in areaswhere no other data is available.

As an alternative, or addition, to the above method, a plurality ofoptical devices may be disposed adjacent the surface of the body ofwater, with each device having a respective aperture pointed atapproximately a same portion of the surface. The devices, or collectingoptics of the devices, are positioned such that each of the respectiveapertures is directed at the surface at a different orientation (e.g.,at different angles relative to azimuth and/or zenith, resulting in adifferent viewing geometry). Data corresponding to a spectrum of lightreceived through each aperture is sensed by sensing assemblies of thedevices. Based on the sensed data, a spectrum of light originatingunderneath the surface of the water is determined. For example, a totaldownwelling irradiance from the sky may be collected (e.g., using adiffuser/cosine corrector and/or reflectance plaque), and thewater-leaving radiance may be calculated based on data sensed by thesurface-directed devices and the total downwelling irradiance.

Calculating the water-leaving radiance may include, e.g., modeling abidirectional reflectance distribution function for the surface of thewater. The bidirectional reflectance distribution function may bedetermined based on, e.g., the solar zenith and azimuth (determined bymeasurements, date and time and geographic position of measurement,and/or any other suitable factors), device zenith and azimuth, field ofview, surface roughness (based e.g. on wind speed), and a suitable modelof light behavior at the water-sky interface. A suitable model of lightbehavior at the water-sky interface may include, e.g. transmittance andreflectance amplitudes, phase, and polarization at the interface basedon Fresnel equations, Maxwell's equations, and/or any other suitablemodel.

Based on the bidirectional reflectance distribution function of thesurface, the amount of surface-reflected radiance that each device istheoretically expected to measure may be calculated. This theoreticalprediction may be compared to the radiance actually measured by eachdevice, which includes both surface-reflected radiance and water-leavingradiance. Based on the theoretical prediction, the differences insignals measured by the devices viewing the same portion of the surfacefrom different directions may be used to obtain an estimate of thecontribution of the surface-reflected radiances to the measured signals.Based on this estimate, the water-leaving radiance corresponding to eachdevice is calculated (e.g., by subtracting the estimatedsurface-reflection contributions from the measured signals). Theremote-sensing reflectance corresponding to each device may be obtainedby dividing the water-leaving radiance by the total sky irradiance.

Alternatively, the water-leaving radiance may be calculated without anexplicit model of the bidirectional reflectance distribution functionfor the surface of the water. For example, the relationship between theupwelling radiances measured by the plurality of devices may beestimated (based on, e.g., the measurement angles of the devices), andthis relationship may be used to estimate the water-leaving radiance.Estimating the relationship between the measured upwelling radiances mayinclude using a linear or nonlinear fit on the measured data and/or asimulation predicting the radiances. In some examples, at least two ofthe devices are configured to have viewing angles at which thewater-leaving radiances are predicted (e.g., by angular symmetry at agiven time of day and location) to be substantially equal. This maysimplify the calculation of the relationship between the measuredradiances.

According to this method, no sky-directed aperture is required, thoughone may optionally be used.

I. Illustrative Method for Acquiring Hyperspectral Data Above Water andUnderwater

This section describes steps of an illustrative method 700 for acquiringhyperspectral data above water and underwater using a single device; seeFIG. 19. Aspects of hyperspectral sensing systems and devices describedabove (e.g., device 30) may be utilized in the method steps describedbelow. Where appropriate, reference may be made to components andsystems that may be used in carrying out each step. These references arefor illustration, and are not intended to limit the possible ways ofcarrying out any particular step of the method.

FIG. 19 is a flowchart illustrating steps performed in an illustrativemethod, and may not recite the complete process or all steps of themethod. Although various steps of method 700 are described below anddepicted in FIG. 19, the steps need not necessarily all be performed,and in some cases may be performed simultaneously or in a differentorder than the order shown.

At step 702, the method includes positioning a hyperspectral sensingdevice underwater. Typically, the entire device is immersed in water(e.g., in a body of water such as an ocean), but in some examples, onlya portion of the device is immersed (e.g., the optical collector). Thedevice typically includes a wireless communications module and a powersupply. Accordingly, when the device is deployed underwater, it need notbe connected by a power cable or data cable to another platform ordevice (e.g., a ship on the water's surface). This reduces the risk thatanother platform or device will cast a shadow over the area beingsampled, or otherwise interfere with the sample.

At step 704, the method includes acquiring a first portion ofhyperspectral data underwater. Acquiring the data typically includescollecting light (e.g., ambient light) with a collector of the deviceand transferring the collected light to a suitable sensor of the device(e.g., a compact spectrometer). Acquiring the data may further includescanning a region or object of interest (e.g., by adjustment of thecollector to collect light from different spatial areas or directions,by transporting the device on an underwater vehicle such that thecollector collects light from different areas, and/or the like).

At step 706, the method includes positioning the device above water(e.g., moving the device from an underwater position to an above-waterposition). The above-water position may comprise a ship, buoy, platform,shore, or other suitable location near the water. Alternatively, theabove-water position may be located far from the water in which thedevice had been immersed. In some examples, the above-water position isin the air (e.g., with the device mounted on an unmanned aerial vehicleor other suitable device).

At step 708, the method includes acquiring a second portion ofhyperspectral data above water. Acquiring the data includes collectinglight and may include scanning a region or object of interest, asdescribed above with reference to step 704. The sensor of the device isconfigured to acquire data both underwater and above-water. For example,the sensor is sufficiently sensitive to acquire data with a suitablesignal-to-noise ratio in underwater environments, which are typicallyassociated with low light levels, and is also capable of acquiring datain bright above-water environments (e.g., the sensor has a high dynamicrange). In some examples, one or more settings of the sensor and/orcollector are changed for the acquisition of the second portion of data.For example, an integration time, sampling rate, aperture size, filterselection, and/or other suitable parameter may be different when thedevice acquires data above water compared to when the device acquiresdata underwater. Such a parameter may be varied automatically by acontroller on board the device (e.g., in response to measuring a higherlight level, lower external pressure, and/or other suitable indication)and/or may be changed by a user (e.g., a user communicating with thecontroller via an external computer). In some cases, the collector usedunderwater is replaced with a different collector when the device isremoved from the water, but the same spectrometer is used in bothenvironments.

At step 710, the method optionally includes logging the acquired firstand second data portions on a memory store of the device.

In some examples, steps 706-708 are performed prior to steps 702-704.That is, the above-water measurements may be performed prior toimmersing the device in water and performing the underwatermeasurements.

In some examples, the steps of positioning the device above water andpositioning the device underwater include deliberately moving the deviceto selected locations. Alternatively, these steps may occurautomatically as the device is immersed in water due to changingenvironmental conditions. For example, the device may disposed on aplatform that is sometimes immersed in water (e.g., due to changingtides).

J. Illustrative Embedded Microcontroller Architecture

In some examples, electronics module 70 comprises an embedded-systemsmicrocontroller architecture 800. Power supply 90 and/or input/outputhub 80 may be integrated into embedded microcontroller architecture 800.An example embedded microcontroller architecture 800 is depictedschematically in FIG. 20.

Using embedded microcontroller architecture 800, hyperspectral sensingdevice 30 is powered by a DC regulator from a power supply such as aninternal battery or an auxiliary power supply, which provides stablepower output independent of the changes in the electrical load, and canbe provided via USB, inductive charging or external power source. Thepower supply is configured to meet the demand of the on-board sensors,and therefore a DC step-up voltage amplifier can be used to increase theoutput of a low-voltage battery (such as 3.7V LiPoly/Lilon) to anoperating voltage of, for example, 5V. A DC-DC converter can also beused to lower and regulate a higher-voltage DC power supply (for example12V) to an operating voltage of 5V, or to multiple voltage levelsselected for beneficial and/or optimal operation of various sensors anddevices. The power is supplied to the micro-controller unit (MCU),sensors, external analog-to-digital converter circuitry as well asinput-output (I/O) hub 80.

Although many micro-controller units feature an integrated multi-channelanalog-to-digital converter (ADC), one or multiple externalfunction-specific ADCs may be used to address the needs to processanalog signals at high sample rates, in some cases in excess of1-million samples-per-second (SPS) and/or with increased digitalprecision, e.g., in the range of 10-bit to 16-bit, or 18-bit, or higher.The analog to digital conversion can be pre-amplified and conditionedusing any number of gain-amplification, signal-conditioning, and/orelectronics filtering stages, or can be referenced to differentialsignals to boost the signal while suppressing electronic noise. Thedigitized video signal from the ADC (from either the hyper-spectral orother analog sensors) are ingested and processed by the MCU and storedin memory, such as a flash memory storage device, e.g., an SD or microSDcard, or other memory device for later transfer to an external hostcomputer for additional levels of data processing. The data mayadditionally be directly up-linked in real-time (while measurements areprogressing, or at regularly scheduled intervals) to land-based systemsthrough a wireless or wired device communication port, such as USB,WiFi, Bluetooth, Bluetooth LE, GSM, Iridium, etc.

The micro-controller (MCU) supplies the operating voltage, clock andtrigger timing signals to the sensor 40. The supplied clock rate is usedto operate the sensor acquisition and output (video) data rate, whilethe trigger signal is used to initiate and end the optical integration(or image acquisition), based on the control input from the MCU. Thetrigger control operation can be programmed via software or firmware andmay be adjustable for example based on processing of the camera videooutput. Additionally, if multiple hyper-spectral sensors are used, thetrigger signal may be set to sequentially or synchronously acquire datafrom multiple sensors.

In some example triggering schemes, the integration time is determinedreal-time via software control based on inputs from external signalssupplied to the MCU from auxiliary sensors 50. This may for exampleinclude temperature, pressure, GPS location, compass heading, tilt/yaw,etc., allowing the sensor to be set to trigger data acquisitions whenspecific conditions for these parameters are met. For example, the dataacquisitions may be set to trigger operating at specific depth intervalsor at specific sensor tilt angles. The trigger and clock rates may alsobe modulated simply to budget the data output rates or manage powerconsumption during longer operating deployments. Also, the trigger maybe activated at specific times in concert with a specific light source60 or engagement of an optical filter in collector 35 to enable anabsorbance, back-scattering or fluorescence measurement.

In some examples, particularly if data or computational bandwidthlimitation exceed the capability of a single MCU, a more powerfulmicro-processing unit (MPU) or multiple micro-controller units may beused to control and operate driving circuitry for different subsystems,for example for adjustments of the collector aperture, pointing/steeringcontrol of the instrument, image stabilization, or fusing ofmulti-sensor inputs.

K. Illustrative Data Processing System

As shown in FIG. 21, this example describes a data processing system 900(also referred to as a computer, computing system, and/or computersystem) in accordance with aspects of the present disclosure. Dataprocessing system 900 is an illustrative data processing system suitablefor implementing aspects of the hyperspectral sensing systems anddevices described above. More specifically, in some examples, devicesthat are embodiments of data processing systems (e.g., smartphones,tablets, personal computers) may be included on a hyperspectral sensingdevice (e.g., as part of an on-board electronics module) and used tocontrol acquisition of data by a sensor of the device and/or datacommunications between the device and a remote server. Additionally, oralternatively, devices that are embodiments of data processing systemsmay be used to communicate with hyperspectral sensing device(s) to,e.g., program the devices, retrieve data from the devices, etc.

In this illustrative example, data processing system 900 includes asystem bus 902 (also referred to as communications framework). Systembus 902 may provide communications between a processor unit 904 (alsoreferred to as a processor or processors), a memory 906, a persistentstorage 908, a communications unit 910, an input/output (I/O) unit 912,a codec 930, and/or a display 914. Memory 906, persistent storage 908,communications unit 910, input/output (I/O) unit 912, display 914, andcodec 930 are examples of resources that may be accessible by processorunit 904 via system bus 902.

Processor unit 904 serves to run instructions that may be loaded intomemory 906. Processor unit 904 may comprise a number of processors, amulti-processor core, and/or a particular type of processor orprocessors (e.g., a central processing unit (CPU), graphics processingunit (GPU), etc.), depending on the particular implementation. Further,processor unit 904 may be implemented using a number of heterogeneousprocessor systems in which a main processor is present with secondaryprocessors on a single chip. As another illustrative example, processorunit 904 may be a symmetric multi-processor system containing multipleprocessors of the same type.

Memory 906 and persistent storage 908 are examples of storage devices916. A storage device may include any suitable hardware capable ofstoring information (e.g., digital information), such as data, programcode in functional form, and/or other suitable information, either on atemporary basis or a permanent basis.

Storage devices 916 also may be referred to as computer-readable storagedevices or computer-readable media. Memory 906 may include a volatilestorage memory 940 and a non-volatile memory 942. In some examples, abasic input/output system (BIOS), containing the basic routines totransfer information between elements within the data processing system900, such as during start-up, may be stored in non-volatile memory 942.Persistent storage 908 may take various forms, depending on theparticular implementation.

Persistent storage 908 may contain one or more components or devices.For example, persistent storage 908 may include one or more devices suchas a magnetic disk drive (also referred to as a hard disk drive or HDD),solid state disk (SSD), floppy disk drive, tape drive, Jaz drive, Zipdrive, flash memory card, memory stick, and/or the like, or anycombination of these. One or more of these devices may be removableand/or portable, e.g., a removable hard drive. Persistent storage 908may include one or more storage media separately or in combination withother storage media, including an optical disk drive such as a compactdisk ROM device (CD-ROM), CD recordable drive (CD-R Drive), CDrewritable drive (CD-RW Drive), and/or a digital versatile disk ROMdrive (DVD-ROM). To facilitate connection of the persistent storagedevices 908 to system bus 902, a removable or non-removable interface istypically used, such as interface 928.

Input/output (I/O) unit 912 allows for input and output of data withother devices that may be connected to data processing system 900 (i.e.,input devices and output devices). For example, input device 932 mayinclude one or more pointing and/or information-input devices such as akeyboard, a mouse, a trackball, stylus, touch pad or touch screen,microphone, joystick, game pad, satellite dish, scanner, TV tuner card,digital camera, digital video camera, web camera, and/or the like. Theseand other input devices may connect to processor unit 904 through systembus 902 via interface port(s) 936. Interface port(s) 936 may include,for example, a serial port, a parallel port, a game port, and/or auniversal serial bus (USB).

Output devices 934 may use some of the same types of ports, and in somecases the same actual ports, as input device(s) 932. For example, a USBport may be used to provide input to data processing system 900 and tooutput information from data processing system 900 to an output device934. Output adapter 938 is provided to illustrate that there are someoutput devices 934 (e.g., monitors, speakers, and printers, amongothers) which require special adapters. Output adapters 938 may include,e.g. video and sounds cards that provide a means of connection betweenthe output device 934 and system bus 902. Other devices and/or systemsof devices may provide both input and output capabilities, such asremote computer(s) 960. Display 914 may include any suitablehuman-machine interface or other mechanism configured to displayinformation to a user, e.g., a CRT, LED, or LCD monitor or screen, etc.

Communications unit 910 refers to any suitable hardware and/or softwareemployed to provide for communications with other data processingsystems or devices. While communication unit 910 is shown inside dataprocessing system 900, it may in some examples be at least partiallyexternal to data processing system 900. Communications unit 910 mayinclude internal and external technologies, e.g., modems (includingregular telephone grade modems, cable modems, and DSL modems), ISDNadapters, and/or wired and wireless Ethernet cards, hubs, routers, etc.Data processing system 900 may operate in a networked environment, usinglogical connections to one or more remote computers 960. A remotecomputer(s) 960 may include a personal computer (PC), a server, arouter, a network PC, a workstation, a microprocessor-based appliance, apeer device, a smart phone, a tablet, another network note, and/or thelike. Remote computer(s) 960 typically include many of the elementsdescribed relative to data processing system 900. Remote computer(s) 960may be logically connected to data processing system 900 through anetwork interface 962 which is connected to data processing system 900via communications unit 910. Network interface 962 encompasses wiredand/or wireless communication networks, such as local-area networks(LAN), wide-area networks (WAN), and cellular networks. LAN technologiesmay include Fiber Distributed Data Interface (FDDI), Copper DistributedData Interface (CDDI), Ethernet, Token Ring, and/or the like. WANtechnologies include point-to-point links, circuit switching networks(e.g., Integrated Services Digital networks (ISDN) and variationsthereon), packet switching networks, and Digital Subscriber Lines (DSL).

Codec 930 may include an encoder, a decoder, or both, comprisinghardware, software, or a combination of hardware and software. Codec 930may include any suitable device and/or software configured to encode,compress, and/or encrypt a data stream or signal for transmission andstorage, and to decode the data stream or signal by decoding,decompressing, and/or decrypting the data stream or signal (e.g., forplayback or editing of a video). Although codec 930 is depicted as aseparate component, codec 930 may be contained or implemented in memory,e.g., non-volatile memory 942.

Non-volatile memory 942 may include read only memory (ROM), programmableROM (PROM), electrically programmable ROM (EPROM), electrically erasableprogrammable ROM (EEPROM), flash memory, and/or the like, or anycombination of these. Volatile memory 940 may include random accessmemory (RAM), which may act as external cache memory. RAM may comprisestatic RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), doubledata rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), and/or the like,or any combination of these.

Instructions for the operating system, applications, and/or programs maybe located in storage devices 916, which are in communication withprocessor unit 904 through system bus 902. In these illustrativeexamples, the instructions are in a functional form in persistentstorage 908. These instructions may be loaded into memory 906 forexecution by processor unit 904. Processes of one or more embodiments ofthe present disclosure may be performed by processor unit 904 usingcomputer-implemented instructions, which may be located in a memory,such as memory 906.

These instructions are referred to as program instructions, programcode, computer usable program code, or computer-readable program codeexecuted by a processor in processor unit 904. The program code in thedifferent embodiments may be embodied on different physical orcomputer-readable storage media, such as memory 906 or persistentstorage 908. Program code 918 may be located in a functional form oncomputer-readable media 920 that is selectively removable and may beloaded onto or transferred to data processing system 900 for executionby processor unit 904. Program code 918 and computer-readable media 920form computer program product 922 in these examples. In one example,computer-readable media 920 may comprise computer-readable storage media924 or computer-readable signal media 926.

Computer-readable storage media 924 may include, for example, an opticalor magnetic disk that is inserted or placed into a drive or other devicethat is part of persistent storage 908 for transfer onto a storagedevice, such as a hard drive, that is part of persistent storage 908.Computer-readable storage media 924 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory, that is connected to data processing system 900. In someinstances, computer-readable storage media 924 may not be removable fromdata processing system 900.

In these examples, computer-readable storage media 924 is anon-transitory, physical or tangible storage device used to storeprogram code 918 rather than a medium that propagates or transmitsprogram code 918. Computer-readable storage media 924 is also referredto as a computer-readable tangible storage device or a computer-readablephysical storage device. In other words, computer-readable storage media924 is media that can be touched by a person.

Alternatively, program code 918 may be transferred to data processingsystem 900, e.g., remotely over a network, using computer-readablesignal media 926. Computer-readable signal media 926 may be, forexample, a propagated data signal containing program code 918. Forexample, computer-readable signal media 926 may be an electromagneticsignal, an optical signal, and/or any other suitable type of signal.These signals may be transmitted over communications links, such aswireless communications links, optical fiber cable, coaxial cable, awire, and/or any other suitable type of communications link. In otherwords, the communications link and/or the connection may be physical orwireless in the illustrative examples.

In some illustrative embodiments, program code 918 may be downloadedover a network to persistent storage 908 from another device or dataprocessing system through computer-readable signal media 926 for usewithin data processing system 900. For instance, program code stored ina computer-readable storage medium in a server data processing systemmay be downloaded over a network from the server to data processingsystem 900. The computer providing program code 918 may be a servercomputer, a client computer, or some other device capable of storing andtransmitting program code 918.

In some examples, program code 918 may comprise an operating system (OS)950. Operating system 950, which may be stored on persistent storage908, controls and allocates resources of data processing system 900. Oneor more applications 952 take advantage of the operating system'smanagement of resources via program modules 954, and program data 956stored on storage devices 916. OS 950 may include any suitable softwaresystem configured to manage and expose hardware resources of computer900 for sharing and use by applications 952. In some examples, OS 950provides application programming interfaces (APIs) that facilitateconnection of different type of hardware and/or provide applications 952access to hardware and OS services. In some examples, certainapplications 952 may provide further services for use by otherapplications 952, e.g., as is the case with so-called “middleware.”Aspects of present disclosure may be implemented with respect to variousoperating systems or combinations of operating systems.

The different components illustrated for data processing system 900 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. One or more embodiments of thepresent disclosure may be implemented in a data processing system thatincludes fewer components or includes components in addition to and/orin place of those illustrated for computer 900. Other components shownin FIG. 21 can be varied from the examples depicted. Differentembodiments may be implemented using any hardware device or systemcapable of running program code. As one example, data processing system900 may include organic components integrated with inorganic componentsand/or may be comprised entirely of organic components (excluding ahuman being). For example, a storage device may be comprised of anorganic semiconductor.

In some examples, processor unit 904 may take the form of a hardwareunit having hardware circuits that are specifically manufactured orconfigured for a particular use, or to produce a particular outcome orprogress. This type of hardware may perform operations without needingprogram code 918 to be loaded into a memory from a storage device to beconfigured to perform the operations. For example, processor unit 904may be a circuit system, an application specific integrated circuit(ASIC), a programmable logic device, or some other suitable type ofhardware configured (e.g., preconfigured or reconfigured) to perform anumber of operations. With a programmable logic device, for example, thedevice is configured to perform the number of operations and may bereconfigured at a later time. Examples of programmable logic devicesinclude, a programmable logic array, a field programmable logic array, afield programmable gate array (FPGA), and other suitable hardwaredevices. With this type of implementation, executable instructions(e.g., program code 918) may be implemented as hardware, e.g., byspecifying an FPGA configuration using a hardware description language(HDL) and then using a resulting binary file to (re)configure the FPGA.

In another example, data processing system 900 may be implemented as anFPGA-based (or in some cases ASIC-based), dedicated-purpose set of statemachines (e.g., Finite State Machines (FSM)), which may allow criticaltasks to be isolated and run on custom hardware. Whereas a processorsuch as a CPU can be described as a shared-use, general purpose statemachine that executes instructions provided to it, FPGA-based statemachine(s) are constructed for a special purpose, and may executehardware-coded logic without sharing resources. Such systems are oftenutilized for safety-related and mission-critical tasks.

In still another illustrative example, processor unit 904 may beimplemented using a combination of processors found in computers andhardware units. Processor unit 904 may have a number of hardware unitsand a number of processors that are configured to run program code 918.With this depicted example, some of the processes may be implemented inthe number of hardware units, while other processes may be implementedin the number of processors.

In another example, system bus 902 may comprise one or more buses, suchas a system bus or an input/output bus. Of course, the bus system may beimplemented using any suitable type of architecture that provides for atransfer of data between different components or devices attached to thebus system. System bus 902 may include several types of bus structure(s)including memory bus or memory controller, a peripheral bus or externalbus, and/or a local bus using any variety of available bus architectures(e.g., Industrial Standard Architecture (ISA), Micro-ChannelArchitecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics(IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI),Card Bus, Universal Serial Bus (USB), Advanced Graphics Port (AGP),Personal Computer Memory Card International Association bus (PCMCIA),Firewire (IEEE 1394), and Small Computer Systems Interface (SCSI)).

Additionally, communications unit 910 may include a number of devicesthat transmit data, receive data, or both transmit and receive data.Communications unit 910 may be, for example, a modem or a networkadapter, two network adapters, or some combination thereof. Further, amemory may be, for example, memory 906, or a cache, such as that foundin an interface and memory controller hub that may be present in systembus 902.

L. Illustrative Distributed Data Processing System

As shown in FIG. 22, this example describes a general network dataprocessing system 1000, interchangeably termed a computer network, anetwork system, a distributed data processing system, or a distributednetwork, aspects of which may be used in conjunction with illustrativeembodiments of hyperspectral sensing devices and/or systems. Forexample, hyperspectral sensing devices may transmit acquired data via anetwork and/or may receive instructions via a network. Network 1000 isan example of a network that may be used for data communication withinhyperspectral sensing system 350.

It should be appreciated that FIG. 22 is provided as an illustration ofone implementation and is not intended to imply any limitation withregard to environments in which different embodiments may beimplemented. Many modifications to the depicted environment may be made.

Network system 1000 is a network of devices (e.g., computers), each ofwhich may be an example of data processing system 900, and othercomponents. Network data processing system 1000 may include network1002, which is a medium configured to provide communications linksbetween various devices and computers connected within network dataprocessing system 1000. Network 1002 may include connections such aswired or wireless communication links, fiber optic cables, and/or anyother suitable medium for transmitting and/or communicating data betweennetwork devices, or any combination thereof.

In the depicted example, a first network device 1004 and a secondnetwork device 1006 connect to network 1002, as do one or morecomputer-readable memories or storage devices 1008. Network devices 1004and 1006 are each examples of data processing system 900, describedabove. In the depicted example, devices 1004 and 1006 are shown asserver computers, which are in communication with one or more serverdata store(s) 1022 that may be employed to store information local toserver computers 1004 and 1006, among others. However, network devicesmay include, without limitation, one or more personal computers, mobilecomputing devices such as personal digital assistants (PDAs), tablets,and smartphones, handheld gaming devices, wearable devices, tabletcomputers, routers, switches, voice gates, servers, electronic storagedevices, imaging devices, media players, and/or other networked-enabledtools that may perform a mechanical or other function. These networkdevices may be interconnected through wired, wireless, optical, andother appropriate communication links.

In addition, client electronic devices 1010 and 1012 and/or a clientsmart device 1014, may connect to network 1002. Each of these devices isan example of data processing system 900, described above regarding FIG.21. Client electronic devices 1010, 1012, and 1014 may include, forexample, one or more personal computers, network computers, and/ormobile computing devices such as personal digital assistants (PDAs),smart phones, handheld gaming devices, wearable devices, and/or tabletcomputers, and the like. In the depicted example, server 1004 providesinformation, such as boot files, operating system images, andapplications to one or more of client electronic devices 1010, 1012, and1014. Client electronic devices 1010, 1012, and 1014 may be referred toas “clients” in the context of their relationship to a server such asserver computer 1004. Client devices may be in communication with one ormore client data store(s) 1020, which may be employed to storeinformation local to the clients (e.g., cookie(s) and/or associatedcontextual information). Network data processing system 1000 may includemore or fewer servers and/or clients (or no servers or clients), as wellas other devices not shown.

In some examples, first client electric device 1010 may transfer anencoded file to server 1004. Server 1004 can store the file, decode thefile, and/or transmit the file to second client electric device 1012. Insome examples, first client electric device 1010 may transfer anuncompressed file to server 1004 and server 1004 may compress the file.In some examples, server 1004 may encode text, audio, and/or videoinformation, and transmit the information via network 1002 to one ormore clients.

Client smart device 1014 may include any suitable portable electronicdevice capable of wireless communications and execution of software,such as a smartphone or a tablet. Generally speaking, the term“smartphone” may describe any suitable portable electronic deviceconfigured to perform functions of a computer, typically having atouchscreen interface, Internet access, and an operating system capableof running downloaded applications. In addition to making phone calls(e.g., over a cellular network), smartphones may be capable of sendingand receiving emails, texts, and multimedia messages, accessing theInternet, and/or functioning as a web browser. Smart devices (e.g.,smartphones) may include features of other known electronic devices,such as a media player, personal digital assistant, digital camera,video camera, and/or global positioning system. Smart devices (e.g.,smartphones) may be capable of connecting with other smart devices,computers, or electronic devices wirelessly, such as through near fieldcommunications (NFC), BLUETOOTH®, WiFi, or mobile broadband networks.Wireless connectively may be established among smart devices,smartphones, computers, and/or other devices to form a mobile networkwhere information can be exchanged.

Data and program code located in system 1000 may be stored in or on acomputer-readable storage medium, such as network-connected storagedevice 1008 and/or a persistent storage 908 of one of the networkcomputers, as described above, and may be downloaded to a dataprocessing system or other device for use. For example, program code maybe stored on a computer-readable storage medium on server computer 1004and downloaded to client 1010 over network 1002, for use on client 1010.In some examples, client data store 1020 and server data store 1022reside on one or more storage devices 1008 and/or 908.

Network data processing system 1000 may be implemented as one or more ofdifferent types of networks. For example, system 1000 may include anintranet, a local area network (LAN), a wide area network (WAN), or apersonal area network (PAN). In some examples, network data processingsystem 1000 includes the Internet, with network 1002 representing aworldwide collection of networks and gateways that use the transmissioncontrol protocol/Internet protocol (TCP/IP) suite of protocols tocommunicate with one another. At the heart of the Internet is a backboneof high-speed data communication lines between major nodes or hostcomputers. Thousands of commercial, governmental, educational and othercomputer systems may be utilized to route data and messages. In someexamples, network 1002 may be referred to as a “cloud.” In thoseexamples, each server 1004 may be referred to as a cloud computing node,and client electronic devices may be referred to as cloud consumers, orthe like. FIG. 22 is intended as an example, and not as an architecturallimitation for any illustrative embodiments.

M. Illustrative Data Calibration Methods

With reference to FIGS. 23-24, this section describes aspects ofillustrative data calibration methods for hyperspectral data and/or anyother suitable spectral data, in accordance with aspects of the presentteachings. In general, data calibration methods described herein allowfor processing measured spectral data (e.g., hyperspectral data measuredby one or more devices 30, and/or any other suitable spectral data) toaccount for environmental and/or instrumental conditions potentiallyaffecting the data. Processing the data in this manner, which may bereferred to as calibrating and/or correcting the data, enables ameaningful comparison between sets of data acquired under conditionsknown or suspected to be different. Environmental factors such as airtemperature, air pressure, humidity, etc. can affect the measurement ofdata by a hyperspectral and/or multi-spectral sensing device by, e.g.,affecting optical refractive indices of components and/or spaces withinthe device, the rate of production of dark counts in a sensor of thedevice, etc. Methods of the present teachings process measured data in amanner that controls for these differences. For example, processing twosets of data according to methods of the present teachings allows thetwo sets of data to be quantitatively compared, even if the two datasets were acquired under different and/or unknown environmentalconditions.

Known methods of data calibration for a spectral sensing devicetypically require a user to perform certain specialized measurementswith the device to obtain calibration data (i.e., data enabling propercalibration of the measured data). For example, some known methodsrequire the device to be removed from the body of water or otherlocation where the device was deployed, so that calibration coefficientscan be determined using lab-based measurements. Lab-based measurementsare generally difficult to perform in the field (e.g., because they mayrequire carefully controlled conditions, equipment not easilytransported, etc.). Accordingly, data calibration methods involvinglab-based measurements are inconvenient, time-consuming, and costly toperform, as they require the device to be retrieved from the field andredeployed to the field after obtaining the calibration data. The costand time involved in obtaining the calibration data can be significant,especially for a network of devices deployed in relatively inconvenientareas, such as a body of water. Accordingly, the lab-based calibrationdata can practically be acquired relatively infrequently (e.g.,seasonally, yearly, etc.) This frequency tends to be inadequate forproperly calibrating measured data to account for factors such astemperature and pressure, which can vary significantly throughout asingle day.

Other known methods typically require expensive and/or fragilecomponents to be added to the device, such as an internal light source,a shutter, and/or mechanical steering components enabling the device toorient its input aperture(s) in a specific manner relative to awell-characterized light source such as a reflectance plaque or themoon. These methods increase the complexity and cost of the device, andmay not be robust against environmental factors.

In contrast to known methods, the data calibration methods of thepresent teachings allow data calibration to be performed using thetarget data (i.e., the data acquired by the device for its originalpurpose, such as hyperspectral data acquired for the purpose ofwater-quality assessment). In other words, the calibration data is thetarget data, or a subset of the target data. Accordingly, data suitablefor use as calibration data is obtained much more frequently than inknown methods, and in some cases is obtained continuously. This allowstarget data to be calibrated using calibration data that was obtainedunder substantially similar conditions as the target data (e.g.,obtained at substantially similar times, viewing angles, and/orenvironmental conditions). These advantages are enabled bycharacteristics of the devices of the present teachings (e.g., device30), including: hyperspectral sensing capability; ability to functionautonomously; size, robustness, and power requirements suitable fordeployment in the field; and so on.

The following subsections describe illustrative examples of datacalibration according to the present disclosure.

a. Illustrative Dark-Current Data Calibration

In some examples, data calibration includes estimating the contributionof dark current to the measured data, and removing the estimateddark-current contribution from the measured data to produce a set ofdark-current-corrected data. The term “dark current” refers to a signalgenerated in a light-measuring device that corresponds to somethingother than impinging light. For example, dark current may arise in aCCD, CMOS, or other photosensitive device due to random generation ofelectrons and/or holes (e.g., in semiconductor components of thedevice). Because the dark-current signal does not represent informationrelated to light incident on the detector, it is a spurious signal, andthe accuracy of the light-related measurement can be improved byremoving the dark-current signal from the measured signal.

The amount of dark current present in data measured by a photosensitivedetector (e.g., sensor 40 of hyperspectral sensing device 30, or anyother suitable light detector) typically depends on factors includingmaterial properties of the detector, the temperature of the detector,and the integration time of the measurement (i.e., the time intervalduring which the detector performed a measurement, such as the timeduring which an electronic and/or mechanical shutter was open). Forexample, in many cases the dark-current contribution to the signal isexpected to be proportional to the integration time of the measurement.

By definition, the dark-current contribution to the signal is not causeddirectly by light impinging on the detector. Accordingly, in thepresently described method, a measured signal that is known tocorrespond to a minimal amount of impinging light is attributedsubstantially or entirely to dark current. The value of that measuredsignal is therefore treated as an estimation of the spurious signalproduced by dark current under the conditions of the measurement (e.g.,the temperature and/or other environmental conditions present at thetime of the measurement).

A measured signal corresponding to little or no impinging light (thatis, a dark-current estimate) may be obtained in any suitable manner. Insome examples, the dark-current estimate is obtained from a spectrumthat was measured when light did impinge on the detector. For example,the minimum value of the measured spectrum (e.g., the lowest-valuesignal in the measured spectral data) may be interpreted as thedark-current estimate. In some cases, however, a low-value signal otherthan the actual lowest signal may be used (e.g., to avoid a suspectedsystematic error). Advantageously, the dark-current estimate may beobtained in this manner using a spectrum already measured by thedetector rather than a dedicated measurement made specifically forpurposes of calibration.

The dark-current estimate obtained in this manner may be used tocalibrate other target data measured under similar conditions, or underany other conditions for which the dark-current estimate is a reasonableapproximation to the dark-current contribution to the measured signal.For example, a dark-current estimate obtained using a given integrationtime may be subtracted from a target spectrum measured using asubstantially equal integration time to produce adark-current-calibrated spectrum.

In some examples, a plurality of dark-current estimates are obtained fora respective plurality of different integration times (e.g., obtainedusing target spectra measured at different integration times). Thisallows for calibrating target spectra measured with those integrationtimes using a dark-current estimate obtained at an equal integrationtime. The plurality of dark-current estimates and correspondingintegration times may further be used to compute projected dark-currentestimates for integration times at which no dark-current estimate wasmeasured. For example, data interpolation, machine learning, theoreticalmodels, and/or any other suitable process may be used to obtain computeddark-current estimates based on the measured dark-current estimates.

In some examples, one or more of the dark-current estimates obtainedfrom a measured spectrum is associated with a temperature correspondingto the measurement, as well as (or instead of) with the measurementintegration time. For example, a temperature of the device and/or itsenvironment at the approximate time the spectrum was measured may berecorded and associated with the dark-current estimate. The temperaturemay be obtained from a temperature sensor on board the device, atemperature sensor disposed near the device, or other temperature dataassociated with the location where the device was deployed. This allowsthe dark-current estimate to be used to calibrate spectra that weremeasured at temperatures substantially identical or similar to that ofthe spectrum from which the dark-current estimate was derived. Becausethe device temperature tends to affect the amount of dark currentproduced, this method may improve the accuracy of the dark-currentcorrection.

In some examples, the dark-current estimate is subtracted from eachpoint in the spectrum (e.g., from the signal measured at each wavelengthand/or each pixel) to produce the dark-current-calibrated spectrum. Inother words, the dark-current estimate is treated as a constant offsetto the measured spectrum. In other examples, the dark-current estimateis modified in a wavelength-dependent manner, and/or any other suitablemanner, prior to subtraction from the measured spectrum.

FIG. 23 is a flowchart depicting steps performed in an illustrativemethod 1200 for obtaining dark-current-corrected spectral data. Aspectsof hyperspectral sensing systems and devices described above (e.g.,device 30) may be utilized in the method steps described below. Whereappropriate, reference may be made to components and systems that may beused in carrying out each step. These references are for illustration,and are not intended to limit the possible ways of carrying out anyparticular step of the method.

FIG. 23 is a flowchart illustrating steps performed in an illustrativemethod, and may not recite the complete process or all steps of themethod. Although various steps of method 1200 are described below anddepicted in FIG. 23, the steps need not necessarily all be performed,and in some cases may be performed simultaneously or in a differentorder than the order shown.

At step 1202, method 1200 includes obtaining a plurality of measuredspectra. In some examples, the measured spectra comprise hyperspectralmeasurements obtained using a hyperspectral sensing device (e.g., device30) deployed in a remote location (e.g., on, in, or adjacent a body ofwater). As described above, a hyperspectral spectrum includes aquantitative representation of a signal measured at each of a pluralityof narrow, adjacent spectral bands spanning a suitable wavelength range(e.g., some or all of the visible, ultraviolet, and/or infrared spectrumranges). The signal at each spectral value includes a light level of anylight having the corresponding wavelength and impinging on the detector,as well as the spurious dark-current signal, as described above.Quantitatively, the measured signal may comprise a number of counts, anelectric field strength, a power, and/or an intensity represented in anysuitable units. Suitable units may include physical units, such as apower or energy per unit area, one or more digital numbers associatedwith photodetector bits involved in the measurement, and/or arbitraryunits.

The plurality of measured spectra include spectra measured with at leasttwo respective different integration times (e.g., two, five, ten, ormore different integration times) within a suitable range of integrationtimes (e.g., 0 to 3 seconds, 150 microseconds to 2 seconds, and/or anyother suitable range). Some of the spectra may have been measured usingthe same integration time (i.e., there may be more than one measuredspectrum for some or all of the plurality of integration times).

In some examples, the plurality of measured spectra are transmitted fromthe device (e.g., wirelessly) to a receiver that is remote from thedevice (e.g., a receiver disposed at a distance of several feet, tens offeet, hundreds of feet, thousands of feet, or miles away from thedevice). Alternatively, or additionally, the measured spectra may beoffloaded from a memory store of the device using a wired connection.For example, the measured spectral data may be transferred from thedevice to a computer or storage device, either in the field (e.g.,without removing the device from its remote deployment location) or in adifferent location (e.g., after retrieving the device).

At step 1204, method 1200 optionally includes identifying a subset ofthe measured spectra corresponding to certain measurement conditions. Insome examples, the subset of measured spectra is selected based on knownor suspected atmospheric and/or environmental conditions at the time ofthe measurement. For example, certain atmospheric conditions, such aslow glint, lack of cloud cover, and/or lack of atmospheric contaminantssuch as water and ozone, tend to be more accurately modeled by knownsimulations of the atmosphere. Because atmospheric simulations may beused in conjunction with the acquired data (e.g., for further datacalibration, for water-quality assessment, etc.), it may be advantageousto obtain dark-current estimates from measured spectra corresponding toconditions that allow accurate simulation. Additionally, oralternatively, spectra acquired under these conditions may enable a moreaccurate and/or precise dark-current estimate than spectra acquiredunder some other conditions (e.g., conditions corresponding to highglint and/or a high concentration of atmospheric contaminants).Accordingly, a subset of spectra corresponding to these conditions maybe identified by consulting data relating to atmospheric and/or weatherconditions at the day and time at which the spectra were measured, andselecting spectra measured at times when conditions were preferable.

In some examples, data relating to environmental conditions may beobtained from the device itself. For example, data obtained fromauxiliary sensors on the device such as humidity sensors, ozone sensors,and so on may be used to identify preferable environmental conditions.In some examples, the intensity of light measured by the photosensitivedetector of the device (e.g., an intensity in one of the measuredspectra) may be used to estimate cloud cover and thus to identify sunnydays. Alternatively, or additionally, the data relating to environmentalconditions may be obtained from sensors not associated with the device,from an external database (e.g., from a weather service, researchstation, etc.), and/or from any other suitable source.

Alternatively, or additionally, a subset of the measured spectra may beselected on another suitable basis. In other examples, no subset isselected, and all of the obtained measured spectra are used insubsequent method steps.

At step 1206, method 1200 includes determining, for each of theplurality of measured spectra (or for each spectrum in the subsetidentified at step 1204, if applicable), a lowest value of the measuredsignal. As described above, the lowest value of the measured signalcomprises an estimate of the dark-current contribution to the signal ateach part of the spectrum. Accordingly, step 1206 produces an estimateddark-current contribution for each measured spectrum. The dark-currentestimate is associated with the integration time corresponding to themeasured spectrum.

As an example, suppose the minimum signal of a spectrum measured usingan integration time of 500 microseconds occurs at 480 nanometers (nm)and has a value of 0.05 arbitrary units. In this case, the signalcorresponding to 480 nm is identified as the lowest measured value atstep 1206, and 0.05 arbitrary units is interpreted as an estimate of thedark-current contribution to the signal at all wavelengths. Furthermore,as described above, the estimated dark-current contribution of 0.05 maybe used as an estimate of the dark-current contribution to other spectrameasured with an integration time equal to or nearly equal to 500microseconds.

In some examples, the lowest value of the signal of each spectrum isselected at step 1206 irrespective of the wavelength at which the lowestvalue is found. Alternatively, in some examples the lowest value withina certain subset of the available spectrum is identified at step 1206.This may be the case if, for example, a device malfunction or otherproblem is suspected at certain wavelengths.

Accordingly, step 1206 produces a plurality of dark-current estimates,each estimate being associated with the integration time of the measuredspectrum from which the estimate was obtained. The dark-currentestimates and associated integration times may be represented as orderedpairs of data.

At step 1208, method 1200 includes using the dark-current estimatesobtained at step 1206 to compute a projected dark-current estimatecorresponding to a selected integration time. For example, thedark-current estimates obtained directly from the measured spectra atstep 1206 may be used to obtain a computed dark-current estimatecorresponding to an integration time for which no measured spectrum waspresent at step 1206. In some examples, obtaining one or more computeddark-current estimates based on the direct estimates from step 1206includes fitting a function to the data obtained at step 1206 (e.g.,using polynomial interpolation, spline interpolation, regression, and/orthe like) and using the function to predict a dark-current estimate at aselected integration time. In some examples, obtaining the computeddark-current estimate(s) is performed at least in part by one or moreneural networks, support vector machines, and/or any other suitablemachine learning and/or artificial intelligence.

At step 1210, method 1200 includes correcting a first measured spectrumfor dark current by subtracting from the first measured spectrum a firstdark-current estimate. The first dark-current estimate corresponds tothe integration time associated with the first measured spectrum, andmay also correspond to a temperature or other environmentalcharacteristic associated with the time at which the first spectrum wasmeasured. Subtracting the first dark-current estimate from the firstmeasured spectrum in this example includes subtracting the firstdark-current estimate from every value in the first measured spectrum(e.g., from the signal at every wavelength and/or every pixel in thefirst measured spectrum). Put another way, the entire first measuredspectrum is offset by the first dark-current estimate. A measuredspectrum that has been offset in this manner may be referred to as adark-current-calibrated spectrum and/or a dark-current-correctedspectrum.

In some examples, the first measured spectrum that isdark-current-calibrated at step 1210 is one of the plurality of measuredspectra received at step 1202. In this case, the first dark-currentestimate may be one of the dark-current estimates directly obtained atstep 1206 (e.g., the lowest value of the first measured spectrum), or itmay be a computed dark-current estimate obtained at step 1208. In otherexamples, the first measured spectrum is not one of the plurality ofmeasured spectra received at step 1202, and the first dark-currentestimate is a computed dark-current estimate derived at step 1208.

The dark-current-calibrated spectrum obtained at step 1210 may be usedin any suitable manner. For example, the dark-current-calibratedspectrum may be analyzed to determine quantities relevant towater-quality assessment, such as chlorophyll concentrations.Additionally, or alternatively, the dark-current-calibrated spectrum maybe used to update, calibrate, and/or confirm remote-sensing dataobtained by a satellite or other airborne device from the location wherethe device was deployed. Optionally, prior to being used for these orany other suitable purposes, the dark-current-calibrated spectrum may becalibrated for one or more additional factors, as described below.

b. Illustrative Radiometric Calibration

In some examples, data calibration in accordance with aspects of thepresent teachings includes radiometric data calibration, AKA radiance orirradiance calibration. Radiometric data calibration allows forcorrecting measured spectra to account for factors affecting themeasurement of light by the sensor that are not directly related to theproperties intended to be observed. For example, consider ahyperspectral sensor deployed adjacent a body of water for the purposeof measuring spectra of light propagating from the water's surface,which can be used for water quality assessment. Light entering thesensor from the water's surface typically has traveled through theatmosphere to the water prior to being scattered or otherwise directedupward back through the water's surface. Accordingly, at least someproperties of the light are at least partly determined by effectsarising from the transfer of light through the atmosphere (e.g.,scattering, absorption, etc.), solar elevation or angle, and/or othereffects not directly related to the water-related characteristic beingassessed. Radiometric calibration tends to correct for these effects,facilitating the comparison of data measured at different times,different geographic locations, and/or by different sensors.

In general, radiometric data calibration of a measured spectrum inaccordance with aspects of the present teachings involves a comparisonbetween the measured spectrum and a simulated spectrum (or spectrumportion). The simulated spectrum or spectral data is calculated based onmodel(s) accounting for atmospheric effects on the measured light,and/or any other suitable effects. The simulation is adjusted (e.g., byvarying one or more simulation parameters such as atmospheric aerosolconcentration, relative humidity, etc.) to substantially match themeasured spectrum according to one or more predefined criteria. Forexample, a nonlinear optimization may be performed such that thedifference between the measured and simulated spectra is substantiallyminimized. Adjusting the simulation such that the simulation predictsspectral data consistent with the measured data tends to make thesimulation accurately represent the atmospheric and/or surfaceconditions of the measurement. Accordingly, the adjusted simulation isused to calibrate the measured spectrum to account for atmosphericeffects.

Calibrating the measured spectrum generally includes obtaining amathematical transformation between the measured spectrum and thesimulated spectrum produced by the adjusted simulation. Because it isbased on the simulated spectrum corresponding to the adjustedparameters, this transformation inherently tends to account foratmospheric, surface, and/or environmental effects (e.g., solar azimuth,solar elevation, etc.). Suitable mathematical transformations mayinclude linear or nonlinear regressions, ratios, convolutions, and/orarithmetic differences. Applying the obtained mathematicaltransformation to the measured spectrum produces a radiometricallycalibrated spectrum. For example, the measured spectrum may be regressedagainst a simulated spectrum produced by the adjusted simulation toproduce a radiometrically calibrated spectrum. In some examples, themathematical transformation may additionally or alternatively be appliedto a second measured spectrum (e.g., a spectrum measured under similarenvironmental and/or atmospheric conditions as the measured spectrum onwhich the transformation is based) to radiometrically calibrate thesecond measured spectrum.

A radiometrically calibrated spectrum may be analyzed for water-qualityassessment purposes and/or any other suitable purpose(s) withoutconfounding atmospheric effects. Additionally, or alternatively, acalibrated spectrum may be meaningfully compared to another calibratedspectrum obtained under different conditions (e.g., at a different timeand/or place, with a different sensor, etc.).

FIG. 24 is a flowchart depicting steps performed in an illustrativemethod 1300 of obtaining radiometrically corrected spectral data.Aspects of hyperspectral sensing systems and devices described above(e.g., device 30) may be utilized in the method steps described below.Where appropriate, reference may be made to components and systems thatmay be used in carrying out each step. These references are forillustration, and are not intended to limit the possible ways ofcarrying out any particular step of the method.

FIG. 24 is a flowchart illustrating steps performed in an illustrativemethod, and may not recite the complete process or all steps of themethod. Although various steps of method 1300 are described below anddepicted in FIG. 24, the steps need not necessarily all be performed,and in some cases may be performed simultaneously or in a differentorder than the order shown.

At step 1302, method 1300 includes obtaining data representing aspectrum measured by a photosensitive device (e.g., a hyperspectralsensing device such as device 30, and/or any other suitable device). Forexample, the spectrum may be a spectrum of downwelling solar irradiance,upwelling radiance from a water body, and/or any other suitablespectrum. The spectrum may comprise hyperspectral data, multispectraldata, and/or any other suitable spectral data. The measured spectrum maybe obtained directly or indirectly from the device.

In some examples, the obtained spectrum was measured under environmentaland/or atmospheric conditions that enable especially accurate modeling.For example, the spectrum may have been measured on a clear sunny daywith relatively small amounts of atmospheric contaminants. At least someknown methods of simulating the behavior of light in the atmosphere tendto be most accurate under these conditions. However, a spectrum measuredunder any suitable conditions may be used.

At step 1304, method 1300 optionally includes calibrating the measuredspectrum for dark current, as described above with reference to method1200. Step 1304 may be omitted if, e.g., the dark-current calibrationwas performed prior to obtaining the measured spectrum at step 1302, orif the dark-current calibration is unnecessary or undesired for someother reason.

At step 1306, method 1300 optionally includes performing a near-infraredcorrection on the measured spectrum. A near-infrared correction may beperformed in cases where it is known or suspected that absorption ofnear-infrared light by water strongly affects the measured spectrum.Liquid water tends to very strongly absorb light having near-infraredwavelengths and/or mid-infrared wavelengths (e.g., light havingwavelengths approximately within a range of 700 nm to 3000 nm).Accordingly, if the measured spectrum corresponds to light that passedthrough a significant amount of water (e.g., if the spectrum correspondsto upwelling radiance from a clear body of water with little bottomreflectance), it can be inferred that the true upwelling radianceincludes very little or no light in the mid-infrared range, and thatsome or all signals actually measured by the sensor in this wavelengthrange are likely spurious (e.g., they likely correspond to light otherthan upwelling light from the desired angle).

Accordingly, at least a portion of the signal measured in thenear-infrared or mid-infrared (for example, the lowest signal valuemeasured in that wavelength range) may be attributed substantiallyentirely to spurious sources and subtracted from the signal values inthat portion of the spectrum to improve the accuracy of the measurement.Specifically, in example step 1306, the lowest value in the measuredspectrum within the range 700-750 nm is subtracted from all of thevalues within that range. This step comprises a near-infrared correctionto the measured spectrum. The exact wavelength range used may bemodified based on properties of the water, the sensor, and/or any othersuitable properties.

Step 1306 may be omitted if it is unneeded or undesirable (e.g., in asituation where the correction is believed to be inaccurate, such assituations in which it is unlikely that water absorption significantlyattenuated the near-infrared portion of the spectrum).

At step 1308, method 1300 includes normalizing the measured spectrum(typically after any applicable correction at steps 1304 or 1306).Normalizing the measured spectrum includes rescaling the values of themeasured signal (e.g., by multiplying the measured value at eachwavelength by a normalizing factor). This enables sensible comparisonbetween spectra measured at different levels of solar illumination. Insome examples, the measured spectrum is normalized to the largest signalvalue measured. In some examples, the measured spectrum is normalized tothe largest signal value measured between approximately 450 nm and 650nm, which is the brightest wavelength range in many use cases.

At step 1310, method 1300 includes simulating one or morewavelength-dependent radiance or irradiance values (e.g., a spectrum, orone or more portions of a spectrum) expected to be measured by an idealsensor (e.g., in the absence of nuisance signal, sensor imperfections,and/or other effects described above). In some examples, the simulatedspectrum has a wavelength range identical or approximately equal to awavelength range of the measured spectrum. In other examples, thesimulated spectrum has a wavelength range smaller than the wavelengthrange of the measured spectrum (e.g., corresponding to one or moreadjacent and/or non-adjacent portions of the wavelength range of themeasured spectrum).

The simulation may be based on known characteristics of the solarspectrum at the top of the atmosphere (e.g., before passing through theatmosphere), physical and/or chemical models of the atmosphere and thepassage of light through the atmosphere, and/or any other suitableinformation. For example, the simulation may comprise an atmosphericradiative transfer function. The simulation may additionally oralternatively include solar and sensor viewing geometries correspondingto the measurement of the measured spectrum.

The simulation includes at least one adjustable parameter. Suitableadjustable parameters may include characteristics of the atmosphere(e.g., aerosol concentrations, water vapor concentrations, and/or thelike), and/or any other suitable factors expected to affect lighttransfer through the atmosphere.

In some examples, the simulation is based in whole or in part on knownmodels such as the commonly used computer-based models MODTRAN®, 6S or6SV, and/or any other models. Additionally, or alternatively, modelsbased on machine learning or other forms of artificial intelligence maybe used.

In some examples, some or all of the adjustable simulation parameterscorrespond directly to physical properties of the atmosphere, such asconcentrations of certain molecules. Additionally, or alternatively,some or all of the adjustable parameters may comprise model parameters(e.g., weighting factors) that do not directly correspond to a specificphysical property.

In some examples, the simulation of step 1310 includes models known tobe most accurate in clear weather with minimal atmospheric contaminants.For this reason, the spectrum obtained at step 1302 is in some examplesmeasured under these conditions. Additionally, or alternatively, thespectrum may have been measured at or near one or more desired solarangles and/or sensor viewing angles. For example, the spectrum may havebeen measured at solar zenith. This avoids the need for a cosineapproximation in certain calculations, reducing computational complexityand error. In some examples, the spectrum is measured with the sunpositioned substantially at (90°, 40°) polar and azimuthal angles, whichmay tend to minimize the contribution of sky reflectance to thespectrum. This may enable a more accurate measurement of water-leavingradiance (e.g, in examples where the spectrum corresponds to upwellinglight from a surface of a body of water). However, any suitable spectrummay be used.

Initial values of the adjustable simulation parameters may be selectedin any suitable manner. For example, one or more parameters may beselected to approximately fit the simulated values to one or morebenchmark features of the measured spectrum. (As described below, abetter fit is typically achieved at step 1312.) Additionally, oralternatively, the initial values of the parameters may be chosenrandomly, arbitrarily, and/or by any other suitable method.

Suitable benchmark features may include, e.g., Fraunhofer lines of thesolar spectrum, absorption lines of O₂, H₂O, and/or the like. A suitablebenchmark feature may be selected based on characteristics of themeasured spectrum. For example, if the spectrum corresponds to upwellingradiance from a body of water, and the spectrum was measured with adevice disposed relatively near the water's surface (e.g., within tenmeters of the surface), then the O₂-A absorption band may be a suitablebenchmark, because the amount of light that could have been absorbed byO₂ between the water's surface and the sensor is negligible. If, on theother hand, the spectrum was measured by a sensor disposed at a greaterdistance above the surface, then a non-negligible amount of lightleaving the surface may have been absorbed by the atmosphere beforereaching the sensor. In this case, the simulated values of radiance orirradiance near the O₂ band are not expected to match the measuredspectrum, so a different benchmark is selected.

In some examples, one or more initial values or seed values for thesimulations are obtained based on a depth of a benchmark absorptionline. For example, the method may include identifying a first signalvalue at a first side of the line, a second signal values at a secondside of the line, and a third value within the absorption line. Adifference between the first and third value and a difference betweenthe second and third value is obtained. These differences are related tothe absorption depth of the benchmark line. An estimated opticalthickness of key atmospheric components is obtained based on thedifferences. For example, if the benchmark is an O₂ absorption line, theoptical thickness of O₂ in the atmosphere is obtained. The opticalthickness may be calculated based on a model of propagation of lightthrough the atmosphere. For example, the optical thickness may becalculated as a decay constant in a model based on exponential decay(e.g., Beer's Law). The calculated optical thickness, and/or one or moreatmospheric characteristics derived therefrom, may be used as an initialparameter value for the simulation. An initial value obtained in thismanner tends to be relatively close to the value ultimately obtainedthrough iterative adjustment (see below), which helps any fitting and/oroptimization methods involved in the adjustment to converge.

At step 1312, method 1300 includes iteratively adjusting one or moresimulation parameters such that the simulated values fit the one or morebenchmark features of the measured spectrum according to one or morepredefined criteria (e.g., within one or more predefined tolerances).Iteratively adjusting the simulation parameters to fit the simulatedvalues to the measured spectrum may include, e.g., interpolating thesimulated values across the benchmark line, fitting one or more otherradiance (or irradiance) values to the measured spectrum simultaneously,nonlinear optimization, and/or any other suitable adjustment. Anysuitable fitting procedure may be used. In some examples, parameters areiterated until the respective simulated values of one or more benchmarkabsorption lines are below predefined threshold(s) (e.g., absorptiondepths).

At step 1314, method 1300 optionally includes accounting for a sensorresponse function associated with the measuring device. In general, asensor response function quantitatively characterizes the signalproduced by the sensor in response to a given input. An ideal sensorwould produce a signal that exactly reproduces the input, but a realphysical sensor produces a signal that inevitably deviates at leastslightly from the input. The deviations may be due to factors such asloss and dispersion within the photosensor and associated optics,limited resolution or sensitivity of the detector, and/or the like.Mathematically, the signal produced by the sensor comprises aconvolution of the input with the sensor response function. If thesensor response function of the device that measured the spectrumobtained at step 1302 is known or may be reasonably approximated, it maybe accounted for at optional step 1314. For example, a simulatedspectrum or portion of a spectrum produced by the adjusted simulationmay be convolved with the sensor function to produce an even moreaccurate prediction of the measured spectrum. Alternatively, oradditionally, the measured spectrum and the sensor response function maybe deconvolved, producing a modified version of the measured spectrumthat accounts for instrumental effects. This enables a better comparisonto the simulation.

At step 1316, method 1300 includes obtaining a mathematicaltransformation between the simulated and measured spectra, andtransforming the measured spectrum using the transformation to produce aradiometrically calibrated spectrum. For example, step 1316 may includelinearly regressing the measured spectrum to produce a calibratedmeasurement. The mathematical transformation may additionally oralternatively be used to transform another measured spectrum into acalibrated spectrum.

Methods 1200 and 1300 may be performed using any suitable device.However, using one or both of these methods to calibrate data obtainedusing hyperspectral sensing device 30, described above, may beespecially advantageous. As described above, device 30 is configured tobe deployed in a remote location (e.g., on a body of water) and tocollect data substantially continuously under a wide range ofillumination conditions and in a wide range of environmental conditions.For example, the device has a small size, low power requirements, few orno moving mechanical parts, short data sampling intervals, autonomousfunctioning capability, adjustable integration time (which effectivelyamounts to a dynamic gain), the ability to change operating parametersbased on sensed conditions, and so on. Because the device cancontinuously measure spectra even in difficult conditions, and becausethe methods of the present disclosure include using the measured spectrato perform calibrations, data suitable for use in calibration iscontinuously produced and is available for many different environmentalconditions. No known device, system, or method has these advantages.

N. Illustrative Method for Measuring Water-Leaving Radiance

With reference to FIG. 25, this section describes an illustrative methodof measuring water-leaving radiance (e.g., for the purpose ofcalculating a remote-sensing reflectance, or for any other suitableassessment). As described above, water-leaving light comprises lightemerging from beneath the surface of a body of water (e.g., light fromthe sky that traveled beneath the surface and was eventually scatteredor otherwise emitted upward through the surface), and by definition doesnot include light that was reflected directly from the surface of thewater substantially without traveling underwater. Measuringwater-leaving radiance typically includes detecting a total upwellinglight (comprising, e.g., the water-leaving light together with anysurface-reflected light and/or any light scattered from the atmosphereinto the detector without reaching the water at all), and correcting themeasured upwelling light spectrum to obtain a spectrum corresponding tosubstantially only the water-leaving light. Aspects of methods ofcorrecting the upwelling light measurement are discussed above inSection D. Another method of correcting an upwelling light measurementby iteratively adjusting a simulation is described below.

FIG. 25 is a flowchart depicting steps performed in an illustrativemethod 1400 of obtaining an accurate measurement of water-leavingradiance. Aspects of hyperspectral sensing systems and devices describedabove (e.g., device 30) may be utilized in the method steps describedbelow. Where appropriate, reference may be made to components andsystems that may be used in carrying out each step. These references arefor illustration, and are not intended to limit the possible ways ofcarrying out any particular step of the method.

FIG. 25 is a flowchart illustrating steps performed in an illustrativemethod, and may not recite the complete process or all steps of themethod. Although various steps of method 1400 are described below anddepicted in FIG. 25, the steps need not necessarily all be performed,and in some cases may be performed simultaneously or in a differentorder than the order shown.

At step 1402, method 1400 includes obtaining a measured spectrum ofupwelling light above a surface of a body of water. In some examples,the spectrum is measured by hyperspectral sensor 30 based on lightreceived at a water-directed aperture of the sensor, the aperture beingpositioned at a relatively small distance above the surface of thewater. However, in other examples, the spectrum may be obtained in anyother suitable way. For example, the upwelling light may be measured ata greater distance above the water, and/or with a different type ofinstrument. Obtaining the spectrum may include measuring the spectrumand/or receiving the measured spectrum directly or indirectly from themeasurement device.

At step 1404, method 1400 optionally includes obtaining a measuredspectrum of downwelling light above the surface of the water. In someexamples, the spectrum is measured by device 30 (e.g., based on lightreceived at a sky-directed aperture of the sensor). However, in otherexamples, the spectrum may be obtained in any other suitable way. Themeasured downwelling spectrum and the measured upwelling spectrum may ormay not be measured using the same device.

In some examples, only the upwelling light spectrum is obtained. Thatis, step 1404 may be omitted. However, it is common to obtain both thedownwelling and upwelling spectra to facilitate calculating a quantitythat depends on both the downwelling and upwelling light. An example ofsuch a quantity is the remote-sensing reflectance, discussed in SectionD.

At step 1406, method 1400 includes simulating at least a portion of anupwelling light spectrum using a simulation having at least oneadjustable parameter. In examples wherein step 1404 was performed, adownwelling spectrum or spectrum portion may additionally (oralternatively) be obtained from the simulation. The simulated spectrumvalues represent a theoretical and/or computational prediction of thespectrum of downwelling (or upwelling) light above the surface of thebody of water.

At step 1406, the simulation may or may not include parameters, models,or other aspects that are specifically configured to represent theconditions under which the spectra obtained at steps 1402 and 1404 weremeasured. For example, the simulation may include values of temperature,pressure, solar angle, and/or other parameter(s) corresponding to thetime and place the spectra were measured, or they may include estimated,standard, and/or arbitrary values. As discussed below, at step 1408, thesimulation parameters are adjusted.

Simulating the spectrum or spectrum portion(s) at step 1406 may include,e.g., running a computer-implemented simulation based on one or moreaspects of the atmosphere and/or the body of water. For example, step1406 may include establishing an atmospheric transfer function modelingthe propagation of light through one or more layers of the atmosphere.The atmospheric transfer function may predict light transfer through anysuitable portions of the atmosphere and/or substantially all layers ofthe atmosphere (e.g., from the top of the atmosphere to the surface ofthe body of water). Performing the simulation may include usingMODTRAN®, 6S or 6SV, and/or any suitable model(s).

Simulating the spectral values at step 1406 may additionally oralternatively include simulating an interaction of light with thesurface (and/or near-surface portions) of the body of water. Forexample, step 1406 may include establishing a bidirectional reflectancedistribution function, as described above in Section D.

In general, any suitable model or (combination of models) having atleast one adjustable parameter may be used at step 1406 to obtain thesimulated spectra or spectrum portion(s). Adjustable parameters mayinclude characteristics of the atmosphere (e.g., aerosol concentrations,water vapor concentrations, and/or the like), and/or any other suitablefactors. Initial values of the adjustable parameters may be selectedusing any suitable method(s), and may reflect the conditions of themeasurement to any extent.

At step 1408, the method includes iteratively adjusting the simulationby adjusting one or more adjustable simulation parameters such that thesimulated spectra (or spectra portions) match the measured spectraaccording to one or more predefined criteria. For example, simulatedparameters may be adjusted until one or more benchmark features of thesimulated spectra are fit to the corresponding benchmark feature in themeasured upwelling or downwelling spectra. Suitable benchmark featuresmay include absorption lines corresponding to Fraunhofer solar lines, O₂absorption lines, H₂O absorption lines, and/or any other spectralfeatures expected to be present and recognizable in the measured andsimulated spectra. The simulation may be iterated until the simulatedbenchmark and the measured benchmark agree within a predeterminedtolerance.

The adjusted simulation, including the adjusted parameters that resultin a fit between the simulated and measured spectra, is taken to be anaccurate simulation of the atmosphere and/or body of water at the timeof the measurement.

At step 1410, the method includes determining an accurate spectrum ofthe water-leaving radiance by correcting the measured upwelling spectrumbased on the adjusted simulation. Correcting the measured upwellingspectrum includes using the adjusted simulation to predict thecontribution to the measured upwelling spectrum by light other thanwater-leaving light. For example, if the upwelling spectrum was measuredby a detector positioned close to the water's surface, correcting themeasured spectrum includes using the adjusted simulation to predict thespectrum of the surface-reflected light, and subtracting the predictedspectrum from the measured spectrum to obtain a corrected measuredspectrum that accurately represents the spectrum of water-leaving light.

In examples wherein path radiance (e.g., light that reaches the detectorwithout having interacted with the water's surface) is expected to benon-negligible, correcting the measured spectrum additionally oralternatively includes using the adjusted simulation to predict thepath-radiance spectrum, and subtracting the path-radiance spectrum fromthe measured upwelling spectrum. In general, any suitable correctionsinvolving an accurate simulation of the atmosphere and/or body of watermay be performed at step 1410.

A corrected measured spectrum obtained by a simulation adjusted in thismanner may be used for any suitable purpose. For example, a correctedmeasured spectrum of water-leaving radiance may be used to calculate aremote-sensing reflectance. Alternatively, or additionally, a correctedmeasured spectrum may be used as a basis for estimating a spectrumcorresponding to another location. For example, a corrected measuredspectrum may be used as “ground-truth” data to update data obtained froman airborne device scanning a body of water where no near-surfacedevices have been deployed.

In some examples, method 1400 is performed in conjunction with method1200 and/or method 1300. In other words, the measured spectrum that iscorrected by method 1400 may be further adjusted to account fordark-current contributions and/or radiometric calibration factors. Insome examples, simulation(s) used for method 1300 may also be used formethod 1400. Method 1400 may be referred to as an atmospheric correctionand/or a preprocessing step.

As described above with reference to methods 1200 and 1300, method 1400may be performed using spectra acquired by any suitable measurementdevice. However, using hyperspectral sensing device 30, may beespecially advantageous due to, e.g., the ability of the device tocontinuously measure spectra even in difficult conditions.

O. Illustrative Functional-Basis Representation of Hyperspectral Data

This section describes illustrative methods involving representations ofspectral data in a functional basis, in accordance with aspects of thepresent teachings. In general, representing spectral data in afunctional basis space includes selecting a suitable functional basisfor the representation and determining, for each data set to berepresented, a function representing the respective data set in theselected basis. Determining the representative function typicallyincludes determining values for one or more weighting coefficientsand/or other parameters, at least some of which may be basis-dependent.A representative function may also be referred to as an approximatingfunction.

A functional basis space comprises a set of basis functions defining abasis space, wherein any suitable function existing within the space isrepresentable by a linear combination of basis functions. (Suitablefunctions for representation within the basis space may includefunctions meeting one or more mathematical requirements, such as beingcontinuous and/or differentiable.) This is analogous in at least somerespects to a vector space defined by basis vectors, wherein each vectorin the space can be represented by a linear combination of basisvectors.

Suitable examples of functional basis spaces include basis spacesdefined by polynomial basis functions, spline basis functions, waveletbasis functions, radial basis functions, and/or any other suitablebases. A functional basis space may be defined by any suitable number offunctions, including an infinite number of functions.

As described above, a spectrum measured by a wavelength-sensitivephotodetector (e.g., a spectrometer of device 30) comprises a set ofdata including, for each spectral band in the spectrum, a quantitativemeasure of the associated light level (e.g., intensity, digital number,and/or the like). In accordance with aspects of the present teachings,the measured spectrum is represented as a linear combination of basisfunctions, wherein the basis functions and the linear combinationthereof are generally functions of wavelength. Additional dataprocessing techniques such as derivative, difference, and/or othervariational analysis methods may optionally be performed on thefunctional-basis representation of the spectrum to increase asignal-to-noise ratio, identify features of the data, and/or tootherwise process and/or analyze the data.

The functional basis representation of measured spectra in accordancewith aspects of the present teachings enables several advantages overknown methods of representing and/or using measured spectra. Forexample, the functional basis representation is typically morecomputationally efficient than a raw data set. In cases wherein themeasured spectrum is hyperspectral, the spectrum may include hundreds orthousands (or more) of spectral bands, so a data set representing thespectrum may become large and unwieldy. A representativefunctional-basis function that maps any wavelength in the domain to thecorresponding light level is a more efficient way of representing thespectral data. Additionally, the function may be implementable in acomputer system in a manner that is better suited for certain purposes(e.g., use in an object-oriented software program) than is a data set.

Another advantage of a functional-basis representation compared to adata set is that known methods of using a data set (e.g., for purposesof water-quality assessment) typically rely on only a subset of thespectral bands in the spectrum. In other words, not all of the data isused in known methods, so any assessment made based on the data seteffectively ignores at least some of the information contained in thespectrum. For example, known methods of computing quantities such aschlorophyll concentration use intensities measured at several discretewavelengths, and discard the rest of the measured spectrum.

In contrast, the functional-basis representation of the spectrum isdetermined using all of the measured spectral data, and the entirety ofthe information contained in the spectrum is necessarily included.Accordingly, the functional-basis representation of the spectrumincorporates aspects of the data, such as subtle patterns or smallfeatures, that are typically lost when using known methods. This makesthe functional-basis representation of the spectrum advantageous forapplications wherein computations are performed based on the spectrum.Such applications may include computing water transparency, turbidity,temperature; concentrations and/or amounts of sediment, chlorophyll,colored dissolved organic matter, nitrogen, phosphorous, particulates,and/or any other suitable quantity.

Another example application is using spectral data acquired by anear-surface sensor (i.e., ground-truth data) to update spectral dataacquired by a remote sensor (e.g., a satellite) from a location where nosuitable corresponding ground-truth data is available. FIG. 26 is aflowchart depicting steps performed in an illustrative method 1500 ofupdating remote data using respective functional-basis representationsof another set of remote data and corresponding ground-truth data.Aspects of hyperspectral sensing systems and devices described above(e.g., device 30) may be utilized in the method steps described below.Where appropriate, reference may be made to components and systems thatmay be used in carrying out each step. These references are forillustration, and are not intended to limit the possible ways ofcarrying out any particular step of the method.

FIG. 26 is a flowchart illustrating steps performed in an illustrativemethod, and may not recite the complete process or all steps of themethod. Although various steps of method 1500 are described below anddepicted in FIG. 26, the steps need not necessarily all be performed,and in some cases may be performed simultaneously or in a differentorder than the order shown.

At step 1502, method 1500 includes acquiring a ground-truth spectrumcorresponding to a first location. In some examples, the ground-truthspectrum is measured using a hyperspectral sensor, such as device 30,deployed at the first location. However, any suitable spectrum may beused.

The sensor that measures the ground-truth spectrum is disposed near thesurface of the location being measured. For example, it may be disposednear the surface of a body of water, near the ground, and/or near thetop of a tree canopy. The spectrum may correspond to an upwellingradiance, a downwelling radiance, and/or any other suitable measurement.In some examples, step 1502 includes calibrating and/or correcting theground-truth spectrum for dark-current contributions, radiometricfactors, and/or any other relevant factors.

At step 1504, method 1500 includes acquiring first remote spectral datacorresponding to the first location at substantially the time theground-truth spectrum was measured. In other words, the first remotedata is temporally coincident with the ground-truth spectrum. The remotedata comprises a spectrum measured by a device that is remote from thesurface where the ground-truth sensor is deployed. For example, theremote device may comprise a satellite, a spectral sensor carried by adrone, and/or any other suitable remote device. The remote device may behyperspectral or multispectral. In some examples, the remote devicecomprises a plurality of sensors having different spectral ranges, suchthat the plurality of devices together comprise a multispectral orhyperspectral sensor. In some examples, step 1504 includes calibratingand/or correcting the first remote spectral data for dark-currentcontributions, radiometric factors, and/or any other relevant factors.

At step 1506, method 1500 includes establishing a ground-truth functionrepresenting the ground-truth spectrum in a functional basis, andestablishing a remote function representing the remote spectral data inthe same functional basis. Establishing the respective functionsincludes determining respective sets of weighting coefficientsassociated with respective basis functions in the linear combinationdefining the functional-basis representation, as well as any parametersthat define the basis functions themselves. As an example, theground-truth spectrum may be represented in a radial basis functionspace using the following illustrative function y(λ):

${y(\lambda)} = {\sum\limits_{i = 1}^{N}{w_{i}{F( {{\lambda - \lambda_{i}}} )}}}$

where i is an index labeling each basis function F(∥λ−λ_(i)∥) in thelinear combination, on is a weighting coefficient characterizing thecontribution of the ith basis function (of N total basis functions) tothe representative function, and λ_(i) is the wavelength of lightassociated with the ith basis function. An example basis function setsuitable for use in this representative function are Gaussian radialbasis functions F(∥λ−λ_(i))=e^(−(ε∥λ−λ) ^(i) ^(∥)) ² , where ε is ashape parameter having any suitable value (based on, e.g., a spectralresolution of the sensor, a spectral range of the detector, and/or anyother suitable factors). The remote spectral data is transformed to arepresentative function in the same basis space as the ground-truthspectrum (in this example, using the same Gaussian radial basisfunctions). In general, the respective functions representing theground-truth spectrum and remote spectrum have different weightingcoefficients.

The ground-truth spectrum and the remote spectral data may betransformed to the respective functional basis representations using anysuitable method (e.g., a fitting method). Depending on the spectra andthe functional basis selected, suitable methods may include regression(e.g., least-squares regression and/or any other suitable regressionmethods), interpolation (e.g., involving a transforming matrix), and/orany other suitable method.

At step 1508, method 1500 includes correlating the ground-truth spectrumto the first remote spectral data. Correlating the ground-truth andfirst remote data comprises determining a quantitative relationship(e.g., a mathematical function, a matrix transformation, a look-uptable, etc.) that predicts weighting coefficients of the ground-truthfunction based on weighting coefficients of the remote function. Inother words, the remote coefficients are inputs to the correlatingrelationship, and predicted ground-truth coefficients are outputs.

Any suitable method may be used to perform the correlation. Suitablemethods may include, without limitation, regression, interpolation,neural networks, etc. In some examples, performing the correlationincludes using a nonlinear optimization method (e.g., a regularizationmethod) to identify a fitting function that maps the remote coefficientsto ground-truth coefficients with high accuracy (e.g., with errorminimized according to some predetermined scheme, such as a Tikhonovregularization).

At step 1510, method 1500 includes obtaining second remote spectral dataand establishing a function representing the second remote spectral datain the functional basis space. The representative function of the secondremote data is established in the same functional basis space as thefunctions representing the first remote data and the ground-truth dataassociated with the first remote data. Establishing the functionrepresenting the second remote data includes identifying weightingcoefficients corresponding to basis functions within the linearcombination defining the representative function.

In some examples, there is no ground-truth data corresponding to thesecond remote spectral data. For example, the second remote spectraldata may be measured by an airborne device passing over a geographiclocation where no near-surface device is deployed. However, any suitableremote spectral data may be used for any suitable purpose.

At step 1512, method 1500 includes using the correlating relationshipdetermined at step 1508 to predict, based on the weighting coefficientsof the second remote-data function, estimated ground-truth weightingcoefficients. The estimated ground-truth weighting coefficients define afunction representing an estimated or projected ground-truth spectrum inthe functional basis space. The estimated ground-truth spectrum is aprediction of a hypothetical near-surface spectrum that could have beenmeasured at the location and time corresponding to the second remotedata. Accordingly, the correlation obtained between the firstground-truth data and the first remote data is used at step 1512 topredict ground-truth data based on second remote data (e.g., remote datacorresponding to a situation where ground-truth data is not available,or is available but needs to be validated, etc.).

Because the functional basis representations of the first remote dataand the first ground-truth data inherently include all the data acquiredin the respective measurement, the predicted ground-truth data estimatedby this method tends to be more accurate and/or precise than predictionsobtained by known methods. The predicted ground-truth data may be usedto calculate a remote-sensing reflectance, chlorophyll concentration,nitrogen concentration, and/or any other suitable quantity, and/or usedfor any other suitable purpose.

P. Illustrative Combinations and Additional Examples

This section describes additional aspects and features of hyperspectralsensing systems, presented without limitation as a series of paragraphs,some or all of which may be alphanumerically designated for clarity andefficiency. Each of these paragraphs can be combined with one or moreother paragraphs, and/or with disclosure from elsewhere in thisapplication, including the materials incorporated by reference in theCross-References, in any suitable manner. Some of the paragraphs belowexpressly refer to and further limit other paragraphs, providing withoutlimitation examples of some of the suitable combinations.

A0. A hyperspectral sensing system comprising an image sensor configuredto measure an intensity of each of a plurality of spectral bands ofimpinging light; an optical collector configured to direct light toimpinge on the image sensor; and an electronics module configured tostore data corresponding to the intensity measured by the image sensor.

A1. The system of A0, wherein the optical collector includes a firstaperture and a second aperture each configured to accept light; and anoptical director configured to direct light accepted by the firstaperture and light accepted by the second aperture to impinge on theimage sensor.

A2. The system of A0, further comprising a light source, and wherein theoptical collector is mounted slidably on a rail such that an anglesubtended by the light source, the optical collector, and a sampleobject may be selectively adjusted.

B0. A hyperspectral sensing system comprising a compact spectrometer; acollector having a first aperture disposed in a first plane andconfigured to receive light, a second aperture disposed in a secondplane and configured to receive light, and an optical directorconfigured to direct the light received by the first and secondapertures to the compact spectrometer; wherein the first and secondplanes are non-planar relative to each other.

B1. The system of B0, further comprising a modulator having a pluralityof openings and a plurality of blocking portions, wherein the blockingportions are configured to block light.

C0. A method for simultaneously performing hyperspectral measurements onlight from two sources, the method comprising receiving a first portionof light via a first entrance aperture of an optical collector;receiving a second portion of light via a second entrance aperture ofthe optical collector; directing the first portion of light to impingeon a sensor configured to a measure wavelength-dependent intensity ofimpinging light; and directing the second portion of light to impinge onthe sensor.

D0. A method for performing a hyperspectral fluorescence measurement,the method comprising positioning a sample, a light source, and anoptical collector such that the sample, in response to illumination fromthe light source, emits light in a direction receivable by the opticalcollector; illuminating the sample using the light source; receivinglight emitted by the sample using the optical collector; and measuringrespective intensities of a plurality of spectral components of thereceived light using a sensor associated with the collector.

D1. The method of D0, wherein positioning the sample, the light source,and the optical collector includes disposing the sample and the opticalcollector underwater.

D2. The method of any one of D0-D1, further comprising disposing withinthe sample one or more tagging agents each configured to bind with apredetermined substance and to emit an identifiable wavelength-dependentfluorescence in response to illumination by the light source.

D3. The method of D2, wherein at least one of the one or more taggingagents comprises a lanthanide-based tag.

E0. Further aspects of an illustrative hyperspectral sensing system aredescribed below:

-   -   The collector comprising a fixed aperture of 0.01 to 5 mm in        between the sample and the sensor.    -   The collector comprising ground-glass (fused silica/SiO2)        diffuser with an entrance aperture (diffuser on sample side and        aperture on sensor side).    -   The collector comprising an 8 degree numerical aperture SiO2        plano convex lens between the sample and detector located at the        primary focal plane.    -   The sensor comprising one or more dispersive elements such as        prisms, waveguides, diffractive optical elements, etc.    -   Use of ‘multi-spectral’ (i.e. discrete wavelength band) sensors        with higher number of bands (˜10-30 bands)    -   Use of light-sensitive detectors (i.e. photodiodes) and optical        color-filters or band-pass filters    -   Actuation, scanning or movement entire system (sensor+collector)        to acquire image of scene larger than single FOV (pixel). In        some examples, the collector could remain stationary relative to        sensor.    -   Use of image stabilization technology to additional improve        acuity of image obtained on a scanning or moving platform.    -   Use of local storage solutions beyond non-volatile memory, flash        memory, SD/micro-SD cards, etc.    -   Use of other data transfer technologies, as applicable,        microwave, laser, etc. communication to carry out the data        streaming/logging functionality.    -   Use of an interface for live-viewing and/or capturing the        measured output (or some analysis of the measured output) on a        separate handheld device (e.g. cell-phone, tablet, computer,        etc.). This can be done via wired (serial, parallel) or wireless        interface.    -   A hyperspectral sensing device mounted on a buoy, weather        station, weather balloon, unmanned aerial vehicle, unmanned        underwater vehicle, watercraft, aircraft, satellite, automobile,        and/or any other suitable platform.

F0. An autonomous optical sensing device comprising a first spectrometerincluding a sensing element; an optical system configured to directambient light incident from a first direction onto the sensing elementof the first spectrometer; and a first controller coupled to the firstspectrometer and configured to automatically trigger data acquisition bythe first spectrometer at selected intervals; wherein the firstspectrometer and the optical system are at least partially encased in acommon housing.

F1. The device of F0, wherein the optical system is further configuredto direct ambient light incident from a second direction onto thesensing element of the first spectrometer.

F2. The device of F1, further comprising an optical modulator at leastpartially encased in the common housing, the optical modulatorconfigured to modulate the ambient light incident from the seconddirection.

F3. The device of any one of F1-F2, further comprising an opticalpolarizer at least partially encased in the common housing, the opticalpolarizer configured to polarize the ambient light incident from thesecond direction.

F3.5 The device of F3, wherein the optical polarizer comprises acircular polarizer.

F4. The device of any one of F0-F3.5, wherein the optical systemcomprises a first lens assembly having a first value of a selectedoptical characteristic.

F5. The device of F4, wherein the first lens assembly is interchangeablewith a second lens assembly having a second value of the selectedoptical characteristic.

F6. The device of any one of F4-F5, wherein the optical characteristicis a field of view.

F7. The device of any one of F0-F6, further comprising a secondspectrometer at least partially encased in the common housing, whereinthe optical system is further configured to direct ambient lightincident from a second direction onto a sensing element of the secondspectrometer.

F8. The device of F7, further comprising a second controller configuredto automatically trigger data acquisition by the second spectrometer atselected intervals and to control transmission of data to a remoteserver by an onboard data processing system.

F9. The device of F8, wherein the second controller is configured towake the data processing system from a standby mode at predeterminedintervals.

F10. The device of any one of F0-F9, further comprising an opticalhomogenizer configured to homogenize light incident from the firstdirection.

F11. The device of any one of F0-F10, further comprising a photovoltaicpanel configured to provide electrical power to the first controller andthe first spectrometer.

F12. The device of any one of F0-F11, wherein the spectral resolution ofthe first spectrometer is 20 nanometers or better.

F13. The device of any one of F0-F12, further comprising at least onesensor usable for calibrating the first spectrometer.

F14. The device of F13, wherein the at least one sensor comprises atemperature sensor.

F15. A plurality of the devices of any one of F0-F14, wherein eachdevice is in communication with a distributed computer network.

G0. A method of assessing water quality, the method comprising receivingambient light through a first aperture of a housing of an optical devicedisposed adjacent a surface of a body of water, the first aperture beingdirected at the surface of the body of water, wherein the light receivedthrough the first aperture includes light reflected from the surface andlight passing through the surface from underneath; receiving ambientlight through a second aperture of the housing, the second aperturebeing directed at the sky, wherein the light received through the secondaperture includes light coming from the sky; directing the lightreceived through the first and second apertures into a sensing assemblydisposed within the housing; sensing, using the sensing assembly, datacorresponding to a spectrum of the light received through the first andsecond apertures; and determining, based on the sensed data, a spectrumof light originating underneath the surface of the water.

G1. The method of G0, wherein the sensing assembly comprises a firstspectrometer and a second spectrometer, and directing the light receivedfrom the first and second apertures into the sensing assembly includesdirecting the light received through the first aperture into the firstspectrometer and directing the light received through the secondaperture into the second spectrometer.

G2. The method of G0, wherein the sensing assembly comprises aspectrometer, and directing the light received through the first andsecond apertures into the sensing assembly includes directing the lightreceived through the first and second apertures into the spectrometer.

G3. The method of any one of G0-G2, further comprising updating, usingthe recorded data, remote-sensing data of the same body of waterobtained by an airborne device.

G4. The method of any one of G0-G3, wherein determining the spectrum oflight originating underneath the surface of the water includes using aMobley surface correction.

G5. The method of any one of G0-G4, wherein determining the spectrum oflight originating underneath the surface of the water includes using abidirectional reflectance distribution function.

H0. A method of assessing water quality, the method comprising receivingambient light through a first aperture of a first optical devicedisposed adjacent a surface of a body of water, the first aperture beingdirected at the surface of the body of water at a first orientation;receiving ambient light through a second aperture of a second opticaldevice disposed adjacent the surface, the second aperture being directedat the surface at a second orientation, wherein the light receivedthrough the first and second apertures includes light reflected from thesurface and light passing through the surface from underneath; directingthe light received through the first aperture into a first sensingassembly of the first device; directing the light received through thesecond aperture into a second sensing assembly of the second device;sensing, using the first and second sensing assemblies, datacorresponding to respective spectra of the light received through thefirst and second apertures; detecting a total downwelling skyirradiance; and determining, based on the sensed data and the detectedtotal downwelling sky irradiance, a spectrum of light originatingunderneath the surface of the water.

H1. The method of H0, wherein detecting the total downwelling skyirradiance includes using a cosine corrector.

H2. The method of any one of H0-H1, wherein the total downwelling skyirradiance is detected using the first optical device.

H3. The method of any one of H0-H1, wherein the total downwellingirradiance is detected using a third optical device.

H4. The method of any one of H0-H3, wherein the first and secondorientations are each defined by a respective azimuth angle and arespective zenith angle.

H5. The method of any one of H0-H3, wherein determining the spectrum oflight originating under the water includes calculating a bidirectionalreflectance distribution function (BRDF) of the surface of the water;estimating, using the BRDF, the contribution of the light reflected fromthe surface to the data sensed by each of the sensing assemblies; andcorrecting the sensed data based on the estimated contributions.

H6. The method of any one of H0-H3, wherein determining the spectrum oflight originating under the water includes estimating a relationshipbetween the data sensed by each of the sensing assemblies anddetermining the spectrum based on the estimated relationship.

H7. The method of H6, wherein the first and second orientations areselected such that radiances of light received through the first andsecond apertures are approximately equal.

J0. A method for accurately measuring a spectrum of water-leaving light,the method comprising measuring a first spectrum of light correspondingto light upwelling from a surface of a body of water; predicting atheoretical upwelling spectrum using a simulation having one or moreadjustable parameters; adjusting one or more of the adjustableparameters of the simulation to produce an adjusted simulation, whereinan adjusted theoretical upwelling spectrum predicted by the adjustedsimulation agrees with the measured first spectrum; estimating, based onthe adjusted simulation, a contribution to the measured first spectrumprovided by non-water-leaving light; and subtracting the estimatedcontribution from the measured first spectrum to obtain a secondspectrum of light corresponding to a water-leaving radiance.

J1. The method of J0, wherein measuring the first spectrum of lightupwelling from the surface includes receiving the upwelling lightthrough a first aperture of a hyperspectral sensing device disposedadjacent the surface, and measuring the first spectrum using aspectrometer of the hyperspectral sensing device.

J2. The method of J1, further comprising: measuring a third spectrum oflight corresponding to light downwelling from the sky by receiving thedownwelling light through a second aperture of the hyperspectral sensingdevice and measuring the downwelling third spectrum using thespectrometer; wherein the first aperture of the hyperspectral sensingdevice is directed toward the surface and the second aperture isdirected toward the sky.

J3. The method of J2, further comprising calculating a remote-sensingreflectance based on the third measured spectrum of downwelling lightand the second spectrum of light.

J4. The method of any one of J0-J3, wherein the simulation includes anatmospheric transfer function including at least one of the one or moreadjustable simulation parameters.

J5. The method of any one of J0-J4, wherein the simulation includes abidirectional reflectance distribution function including at least oneof the one or more adjustable simulation parameters.

J6. The method of any one of J0-J5, wherein the one or more adjustablesimulation parameters include an atmospheric water vapor concentration.

J7. The method of any one of J0-J6, wherein estimating the contributionof non-water-leaving light includes estimating, based on the adjustedsimulation, a path-radiance spectrum.

J8. The method of any one of J0-J7, wherein the measured first spectrumincludes a measured absorption line having a first absorption depth, theadjusted theoretical upwelling spectrum includes a predicted absorptionline having a second absorption depth, and the second absorption depthequals the first absorption depth within a predefined tolerance.

K0. A computer-implemented method for predicting ground-truth datacorresponding to remotely measured data, the method comprising:acquiring a ground-truth spectrum corresponding to light measured at afirst location at a first time; acquiring first remote spectral datacorresponding to the first location at the first time; determining firstweighting coefficients of a ground-truth function representing theground-truth spectrum in a functional basis space; determining secondweighting coefficients of a first remote function representing the firstremote spectral data in the functional basis space; determining acorrelating relationship predicting the first weighting coefficientsbased on the second weighting coefficients; acquiring second remotespectral data and determining third weighting coefficients of a secondremote function representing the second remote spectral data in thefunctional basis space; and using the correlating relationship topredict, based on the third weighting coefficients, projectedground-truth weighting coefficients corresponding to a projectedground-truth function representing a projected ground-truth spectrum inthe functional basis space.

K1. The method of K0, wherein the ground-truth spectrum is ahyperspectral spectrum.

K2. The method of any one of K0-K1, wherein acquiring the ground-truthspectrum comprises measuring a spectrum of light using a hyperspectralsensing device disposed at the first location at the first time.

K3. The method of any one of K0-K2, wherein the first remote spectraldata comprises multi-spectral data measured by a sensor carried by asatellite.

K4. The method of any one of K0-K3, wherein the second remote spectraldata corresponds to a second location different from the first location.

K5. The method of any one of K0-K4, wherein the functional basis spaceis defined by a set of radial basis functions.

K6. The method of K5, wherein the set of radial basis functionscomprises a set of Gaussian radial basis functions.

K7. The method of any one of K0-K6, wherein determining the firstweighting coefficients includes performing a least-squares regression onthe ground-truth spectrum.

K8. The method of any one of K0-K7, wherein determining the correlatingrelationship includes fitting the second weighting coefficients to thefirst weighting coefficients using Tikhonov regularization.

K9. The method of any one of K0-K8, further comprising radiometricallycalibrating the ground-truth spectrum prior to determining the firstweighting coefficients.

K10. The method of any one of K0-K9, further comprising computing, basedon the projected ground-truth coefficients, a parameter related to waterquality of a body of water associated with the second remote spectraldata.

L0. A computer system for assessing water quality based on hyperspectraldata, the system comprising: one or more processors; a memory; and acomputer program including a plurality of instructions stored in thememory and executable by the one or more processors to: compute firstweighting coefficients corresponding to a functional representation of afirst spectral dataset in a functional basis; compute second weightingcoefficients corresponding to a functional representation of a secondspectral dataset in the functional basis; compute a quantitativeoperation performable on the second weighting coefficients to produce anoutput approximating the first weighting coefficients; perform thequantitative operation on third weighting coefficients corresponding toa functional representation of a third spectral dataset in thefunctional basis, thereby producing projected fourth weightingcoefficients corresponding to a functional representation of a projectedfourth spectral dataset in the functional basis; and calculate at leastone property related to water quality based on the projected fourthweighting coefficients.

L1. The system of L0, wherein the first spectral dataset compriseshyperspectral data acquired by a first hyperspectral sensor disposedadjacent a first surface at a first geographical location.

L2. The system of L1, wherein the first surface is a surface of a bodyof water.

L3. The system of any one of L1-L2, wherein the second spectral datasetcomprises data measured by a second sensor remote from the first surfaceand the third spectral dataset comprises data measured by a third sensorremote from a second surface at a second geographical location, suchthat the projected fourth spectral dataset corresponds to a projectedground-truth spectral dataset corresponding to the second geographicallocation.

L4. The system of any one of L0-L3, wherein the at least one propertyrelated to water quality includes a chlorophyll concentration.

M0. A method for measuring a dark-current-corrected spectrum, the methodcomprising: measuring a first spectrum of light using a photosensitivedetector having a first integration time; measuring a plurality ofsecond spectra using the photosensitive detector, each of the secondspectra corresponding to a different integration time of thephotosensitive detector; estimating a respective dark-currentcontribution for each of the second spectra based on a respective lowestvalue of each of the second spectra; computing a first estimateddark-current contribution for the first spectrum based on the estimateddark-current contributions for the second spectra and the firstintegration time; and subtracting the first estimated dark-currentcontribution from each value of the first spectrum to produce adark-current-corrected spectrum.

M1. The method of M0, wherein computing the first estimated dark-currentcontribution includes obtaining a quantitative function predictingdark-current contributions based on input integration times, and usingthe quantitative function to predict the first estimated dark-currentcontribution based on the first integration time.

M2. The method of M1, wherein obtaining the quantitative functionincludes fitting the integration times corresponding to the plurality ofsecond spectra to the estimated dark-current contributions for theplurality of second spectra using polynomial interpolation.

M3. The method of any one of M0-M2, wherein the first spectrum and theplurality of second spectra are all measured within an interval of time,and the photosensitive detector remains deployed at an outdoor locationduring the interval of time.

M4. The method of M3, wherein the plurality of second spectra comprisesa subset of a plurality of third spectra measured during the interval oftime, the plurality of second spectra being selected from the pluralityof third spectra based on respective environmental conditionscorresponding to each of the second spectra.

M5. The method of M4, wherein the environmental conditions correspondingto each of the second spectra are identified based at least partially ondata measured by one or more auxiliary sensors disposed in a devicecontaining the photosensitive detector.

M6. The method of M5, wherein the auxiliary sensors include atemperature sensor.

M7. The method of any one of M0-M6, wherein the photosensitive detectorcomprises a spectrometer having a spectral resolution of 20 nanometersor better.

M8. The method of any one of M0-M7, wherein the photosensitive detectoris deployed adjacent a body of water, the method further comprisingcomputing, based on the dark-current-corrected spectrum, a quantityrelated to water quality of the body of water.

N0. A method of measuring a radiometrically calibrated spectrum using aspectral sensor deployed at an outdoor location without relocating thespectral sensor, the method comprising: measuring a spectrum of lightusing a spectral sensor deployed at an outdoor location; normalizing themeasured spectrum using a selected normalizing factor; computing asimulated spectrum based on a simulation of at least a portion of theatmosphere at the outdoors location, the simulation including one ormore adjustable parameters; adjusting at least one of the adjustableparameters to produce an adjusted simulated spectrum matching themeasured spectrum according to one or more predefined criteria;determining a mathematical transformation capable of transforming themeasured spectrum, such that at least a portion of the transformedmeasured spectrum approximates at least a portion of the adjustedsimulated spectrum; and performing the mathematical transformation onthe measured spectrum to produce a radiometrically calibrated spectrum.

N1. The method of N0, further comprising measuring a second spectrumusing the spectral sensor deployed at the outdoor location andperforming the mathematical transformation on the second spectrum toradiometrically calibrate the second spectrum.

N2. The method of any one of N0-N1, wherein a wavelength range of thesimulated spectrum approximates a wavelength range of the measuredspectrum.

N3. The method of any one of N0-N2, wherein the one or more predefinedcriteria for matching the adjusted simulated spectrum to the measuredspectrum include agreement within a predefined first tolerance of afirst absorption depth of a first measured absorption line of themeasured spectrum and a second absorption depth of a corresponding firstsimulated absorption line of the adjusted simulated spectrum.

N4. The method of N3, wherein the first measured absorption linecorresponds to O2 absorption in the atmosphere.

N5. The method of any one of N3-N4, wherein the one or more predefinedcriteria further include agreement within a predefined second toleranceof a third absorption depth of a second measured absorption line of themeasured spectrum and a fourth absorption depth of a correspondingsecond measured absorption line of the adjusted simulated spectrum, andwherein the first and second measured absorption lines have respectivecentral wavelengths differing from each other by at least 40 nanometers.

N6. The method of any one of N0-N5, wherein computing the simulatedspectrum includes calculating an optical thickness of an atmosphericcomponent based on an absorption line of the measured spectrum, andusing the calculated optical thickness as an initial value of one of theadjustable parameters of the simulation.

N7. The method of any one of N0-N6, further comprising correcting themeasured spectrum for near-infrared absorption by identifying a lowestvalue of a near-infrared portion of the measured spectrum andsubtracting the lowest value from all values within the near-infraredportion.

N8. The method of any one of N0-N7, further comprising deconvolving themeasured spectrum and a sensor response function of the spectral sensorprior to determining the mathematical transformation.

Advantages, Features, and Benefits

The different embodiments and examples of a hyperspectral sensing systemdescribed herein provide several advantages over known solutions foracquiring hyperspectral data of aquatic, aerial, and/or terrestrialenvironments. For example, illustrative embodiments and examplesdescribed herein allow a compact, low-weight hyperspectral sensingsystem having dimensions suitable for deploying on an autonomousvehicle, a remotely operated vehicle, and/or an unmanned aerial vehicle.

Additionally, and among other benefits, illustrative embodiments andexamples described herein allow a hyperspectral sensing device that canbe deployed autonomously, without power cables or data cables.

Additionally, and among other benefits, illustrative embodiments andexamples described herein allow a hyperspectral sensing device that isrelatively insensitive to vibrations (e.g., due to use of a compactspectrometer).

Additionally, and among other benefits, illustrative embodiments andexamples described herein allow a hyperspectral sensing device to beconstructed using a relatively inexpensive compact spectrometer,enabling a network of hyperspectral sensing devices to be deployed atrelatively low overall cost.

Additionally, and among other benefits, illustrative embodiments andexamples described herein allow a simultaneous or near-simultaneousmeasurement of sky radiance, upwelling water radiance, and optionallyradiance of light reflected from a reference plaque. The ability to makethese measurements simultaneously or nearly simultaneously increases theaccuracy and precision of the measurements and any quantities inferredfrom the measurements, such as remote-sensing reflectance.

Additionally, and among other benefits, illustrative embodiments andexamples described herein allow hyperspectral measurements in underwaterenvironments, above-water environments, and aerial environments usingthe same hyperspectral sensing device, which may enable consistencyamong measurements performed in these different environments.Additionally, the ability to perform measurements in these differentenvironments using just one device decreases the amount of equipmentthat must be transported to measurement sites (e.g., carried onwatercraft, buoys, drones, and/or manually carried by personnel).

Additionally, and among other benefits, illustrative embodiments andexamples described herein allow hyperspectral measurements to be madeusing a device having a wider dynamic range than known hyperspectralsensors, such that a single device is configured to acquirehyperspectral data when deployed underwater and when deployed abovewater.

Additionally, and among other benefits, illustrative embodiments andexamples described herein allow a hyperspectral sensing system toautonomously trigger data collection or adjust measurement parametersbased on sensed data such as temperature, pressure, time, location,light levels, salinity, etc.

Additionally, and among other benefits, illustrative embodiments andexamples described herein allow collection optics on a hyperspectralsensing system to be adjusted and/or replaced without adjustment to anysensors (e.g., spectrometers).

Additionally, and among other benefits, illustrative embodiments andexamples described herein allow hyperspectral measurements offluorescence, scattering, and/or absorption.

No known system or device can perform these functions. However, not allembodiments and examples described herein provide the same advantages orthe same degree of advantage.

CONCLUSION

The disclosure set forth above may encompass multiple distinct exampleswith independent utility. Although each of these has been disclosed inits preferred form(s), the specific embodiments thereof as disclosed andillustrated herein are not to be considered in a limiting sense, becausenumerous variations are possible. To the extent that section headingsare used within this disclosure, such headings are for organizationalpurposes only. The subject matter of the disclosure includes all noveland nonobvious combinations and subcombinations of the various elements,features, functions, and/or properties disclosed herein. The followingclaims particularly point out certain combinations and subcombinationsregarded as novel and nonobvious. Other combinations and subcombinationsof features, functions, elements, and/or properties may be claimed inapplications claiming priority from this or a related application. Suchclaims, whether broader, narrower, equal, or different in scope to theoriginal claims, also are regarded as included within the subject matterof the present disclosure.

What is claimed is:
 1. A computer-implemented method for predictingground-truth data corresponding to remotely measured data, the methodcomprising: acquiring a ground-truth spectrum corresponding to lightmeasured at a first location at a first time; acquiring first remotespectral data corresponding to the first location at the first time;determining first weighting coefficients of a ground-truth functionrepresenting the ground-truth spectrum in a functional basis space;determining second weighting coefficients of a first remote functionrepresenting the first remote spectral data in the functional basisspace; determining a correlating relationship predicting the firstweighting coefficients based on the second weighting coefficients;acquiring second remote spectral data and determining third weightingcoefficients of a second remote function representing the second remotespectral data in the functional basis space; and using the correlatingrelationship to predict, based on the third weighting coefficients,projected ground-truth weighting coefficients corresponding to aprojected ground-truth function representing a projected ground-truthspectrum in the functional basis space.
 2. The method of claim 1,wherein acquiring the ground-truth spectrum comprises measuring aspectrum of light using a hyperspectral sensing device disposed at thefirst location at the first time.
 3. The method of claim 1, wherein thefirst remote spectral data comprises multi-spectral data measured by asensor carried by a satellite.
 4. The method of claim 1, wherein thesecond remote spectral data corresponds to a second location differentfrom the first location.
 5. The method of claim 1, wherein thefunctional basis space is defined by a set of radial basis functions. 6.The method of claim 1, wherein determining the first weightingcoefficients includes performing a least-squares regression on theground-truth spectrum.
 7. The method of claim 1, wherein determining thecorrelating relationship includes fitting the second weightingcoefficients to the first weighting coefficients using Tikhonovregularization.
 8. A method for measuring a dark-current-correctedspectrum, the method comprising: measuring a first spectrum of lightusing a photosensitive detector having a first integration time;measuring a plurality of second spectra using the photosensitivedetector, each of the second spectra corresponding to a differentintegration time of the photosensitive detector; estimating a respectivedark-current contribution for each of the second spectra based on arespective lowest value of each of the second spectra; computing a firstestimated dark-current contribution for the first spectrum based on theestimated dark-current contributions for the second spectra and thefirst integration time; and subtracting the first estimated dark-currentcontribution from each value of the first spectrum to produce adark-current-corrected spectrum.
 9. The method of claim 8, whereincomputing the first estimated dark-current contribution includesobtaining a quantitative function predicting dark-current contributionsbased on input integration times, and using the quantitative function topredict the first estimated dark-current contribution based on the firstintegration time.
 10. The method of claim 8, wherein the first spectrumand the plurality of second spectra are all measured within an intervalof time, and the photosensitive detector remains deployed at an outdoorlocation during the interval of time.
 11. The method of claim 10,wherein the plurality of second spectra comprises a subset of aplurality of third spectra measured during the interval of time, theplurality of second spectra being selected from the plurality of thirdspectra based on respective environmental conditions corresponding toeach of the second spectra.
 12. The method of claim 11, wherein theenvironmental conditions corresponding to each of the second spectra areidentified based at least partially on data measured by one or moreauxiliary sensors disposed in a device containing the photosensitivedetector.
 13. The method of claim 10, wherein the photosensitivedetector comprises a spectrometer having a spectral resolution of 20nanometers or better.
 14. The method of claim 8, wherein thephotosensitive detector is deployed adjacent a body of water, the methodfurther comprising computing, based on the dark-current-correctedspectrum, a quantity related to water quality of the body of water. 15.A method of measuring a radiometrically calibrated spectrum using aspectral sensor deployed at an outdoor location without relocating thespectral sensor, the method comprising: measuring a spectrum of lightusing a spectral sensor deployed at an outdoor location; normalizing themeasured spectrum using a selected normalizing factor; computing asimulated spectrum based on a simulation of at least a portion of theatmosphere at the outdoors location, the simulation including one ormore adjustable parameters; adjusting at least one of the adjustableparameters to produce an adjusted simulated spectrum matching themeasured spectrum according to one or more predefined criteria;determining a mathematical transformation capable of transforming themeasured spectrum, such that at least a portion of the transformedmeasured spectrum approximates at least a portion of the adjustedsimulated spectrum; and performing the mathematical transformation onthe measured spectrum to produce a radiometrically calibrated spectrum.16. The method of claim 15, further comprising measuring a secondspectrum using the spectral sensor deployed at the outdoor location andperforming the mathematical transformation on the second spectrum toradiometrically calibrate the second spectrum.
 17. The method of claim15, wherein the one or more predefined criteria for matching theadjusted simulated spectrum to the measured spectrum include agreementwithin a predefined first tolerance of a first absorption depth of afirst measured absorption line of the measured spectrum and a secondabsorption depth of a corresponding first simulated absorption line ofthe adjusted simulated spectrum.
 18. The method of claim 17, wherein theone or more predefined criteria further include agreement within apredefined second tolerance of a third absorption depth of a secondmeasured absorption line of the measured spectrum and a fourthabsorption depth of a corresponding second measured absorption line ofthe adjusted simulated spectrum, and wherein the first and secondmeasured absorption lines have respective central wavelengths differingfrom each other by at least 40 nanometers.
 19. The method of claim 15,wherein computing the simulated spectrum includes calculating an opticalthickness of an atmospheric component based on an absorption line of themeasured spectrum, and using the calculated optical thickness as aninitial value of one of the adjustable parameters of the simulation. 20.The method of claim 15, further comprising correcting the measuredspectrum for near-infrared absorption by identifying a lowest value of anear-infrared portion of the measured spectrum and subtracting thelowest value from all values within the near-infrared portion.